bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of Some Reproductive Performance of Ewes, Livability and Growth Traits of Lambs of Akkaraman in Breeder Flocks in Niğde/Bor Province
Authors: Aksoy Yüksel , Şekeroğlu Ahmet , Duman Mustafa
Published: December 2023

Conducted within the National Sheep and Goat Breeding “subproject: Akkaraman Sheep Breeding Project under farmer conditions in Nigde’s Bor district” between 2018 and 2022, the current study evaluated the reproductive characteristics of ewes as well as the livability and growth traits of lambs. This study investigated some ‘reproductive traits’ of an average of 6000 ewes per year and ‘growth performance and ‘survival traits’ of a total of 30051 head lambs. The birth weight (LBW), 60th-day body weight (BW60), 120th-day body weight (BW120), and 120th-day body weight gain (BWG120) of Akkaraman lambs during the study periods were 4.14±0.19 kg, 18.58±0.03 kg, 31.31±0.02 kg, and 246±2.23 g/lamb/day, respectively. In the study, among the factors affecting the growth characteristics of Akkaraman lambs, only the effect of gender on BWG120 and birth type on BW120 was found to be insignificant. In contrast, the other factors were found to be found to be statistically significant. In the study, it was determined that the number of lambs per Akkaraman ewe giving birth between 2018-2022 varied between 1.03-1.10. The highest livability in Akkaraman lambs was determined in 2018 (96.61%) and the lowest in 2020 (83.21%). As a result, it was found that liveability in Akkaraman lambs was dependent on birth year, the age of the ewe, gender, and birth type.
Integration of Photovoltaic Distributed Generation in Grid Distribution Network: A Literature Review
Authors: Maharmi Benriwati , Syafii Syafii , Azhari Zakri Azriyenni
Published: December 2023

Distributed Generation (DG) will become a future generation system known as a renewable energy source. Renewable energy is a topic being developed due to being environmentally friendly. It fulfills the need for electrical energy to replace fossil energy sources to reduce climate change due to carbon emissions towards net-zero emissions by 2050, reducing climate change by 1.50 C to 20 C. Solar photovoltaic (PV) is an energy source that has been widely developed and has enormous potential; where PV can be integrated off the grid and on the grid. Integrating PV into the network is challenging, so the network remains stable and reliable due to intermittent energy generation. This paper reviews the integration of PV-DG distribution networks. Topology, optimization, and impacts on the distribution network are also discussed in this paper. The literature review results show that PV-DG integrated into the grid has an impact caused by intermittent PV generation.
Fabrication of Smart Meter for Accurate Use in Home and Industry
Authors: Kirui Nicholas , Kagiri Charles , Mulembo Titus
Published: December 2023

This study addresses the challenges posed by conventional energy meters, which rely on manual readings, leading to human errors and inefficiencies. In response to this, a battery-powered smart meter was developed utilizing an STM32 microcontroller, ADE7758 and STPM32 metering integrated circuits (ICs), SIM and ESP32 communication modules, along with a MYSQL database. Real-time energy data from both single and three-phase appliances were collected, and their energy consumption, errors, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) were quantified. The model demonstrated an acceptable accuracy level, with an estimated MAE of approximately 2.912 units and an estimated RMSE of around 4.048 units, particularly in predicting motor power consumption. Additionally, ARIMA forecasting was applied to a three-phase asynchronous motor, revealing an average active motor power of 250.95 watts, indicating precise results over time.
Financial Literacy, Credit Decision-Making, and Fintech
Authors: Kiky Andreas
Published: December 2023

Financial literacy has evolved from basic financial knowledge into competencies to make financial decisions. Financial literacy is also related to credit decisions and business expansion in creating a resilient business in the long run. However, no specific research paper has done a systematic literature review that discusses this term with credit funding decisions to enhance the business. This article provides the latest linked keywords through VOS viewer visualization to help scholars find a research gap for future research in financial literacy and credit funding decisions. The result found four clusters of keywords regarding financial literacy or credit decisions. Despite many studies on this topic, the linked citation discusses SMEs’ performance, the role of financial inclusion in improving shareholder wealth, and fintech usage in improving financial inclusion. The three interconnected ideas provide a future guideline for scholar to find the latest research gap in this topic.
Kombinasi Seleksi Fitur Berbasis Filter dan Wrapper Menggunakan Naive Bayes pada Klasifikasi Penyakit Jantung
Authors: Azizah Siti Roziana , Herteno Rudy , Farmadi Andi , Kartini Dwi , Budiman Irwan
Published: December 2023

Penyakit jantung menjadi salah satu penyebab utama kematian bersama dengan penyakit lainnya. Dalam bidang teknologi, data mining dapat digunakan untuk mendiagnosa suatu penyakit yang bersumber dari data rekam medis pasien. Pada klasifikasi dataset medis, Naive Bayes merupakan salah satu metode terbaik yang digunakan. Tujuan dari penelitian ini adalah untuk mengetahui perbandingan hasil akurasi dari Naive Bayes menggunakan beberapa seleksi fitur yaitu Forward Selection, Backward Elimination, kombinasi union hasil seleksi fitur Forwad Selection dan Backward Elimination, Information Gain, Gain Ratio, dan kombinasi union hasil seleksi fitur Information Gain dengan Gain Ratio. Data yang digunakan dalam penelitian ini adalah data penyakit jantung yang didapatkan dari UCI Machine Learning Repository. Dari implementasi pemodelan yang akan dilakukan menghasilkan nilai akurasi tertinggi sebesar 91.80% pada algoritma Naive Bayes dengan kombinasi union hasil seleksi fitur Information Gain dan Gain Ratio menggunakan perbandingan data latih dan data uji 80:20. Sedangkan akurasi Naive Bayes dengan kombinasi union hasil seleksi fitur Forward Selection dan Backward Elimination hanya memiliki nilai akurasi sebesar 83.61%
 
Abstract
Heart disease is one of the leading causes of death along with other diseases. In the field of technology, data mining can be used to diagnose a disease sourced from patient medical record data. In the classification of medical datasets, Naive Bayes is one of the best methods used. The purpose of this study is to determine the comparison of the accuracy results of Naive Bayes using several feature selections, namely Forward Selection, Backward Elimination, a combination of union of Forwad Selection and Backward Elimination feature selection results, Information Gain, Gain Ratio, and a combination of union of Information Gain feature selection results with Gain Ratio. The data used in this research is heart disease data obtained from the UCI Machine Learning Repository. From the implementation of modeling that will be carried out, the highest accuracy value is 91.80% in the Naive Bayes algorithm with a combination of union of Information Gain and Gain Ratio feature selection results using a ratio of training data and test data of 80:20. While the accuracy of Naive Bayes with a combination of union selection results of Forward Selection and Backward Elimination features only has an accuracy value of 83.61%.
Transformasi Kota Cerdas dalam Mitigasi Banjir: Pemodelan Curah Hujan DKI Jakarta dengan Pendekatan Spatial Vector Autoregressive (SpVAR) dan Pemetaan Bobot Queen Contiguity
Authors: Melanwati Rinda Lolita , Sumarminingsih Eni , Pramoedyo Henny
Published: December 2023

Perubahan iklim dan cuaca ekstrem menjadi tantangan global, termasuk di Indonesia, dengan peningkatan banjir di DKI Jakarta. Penanggulangan membutuhkan peramalan curah hujan yang akurat. Model VAR digunakan untuk memahami hubungan variabel cuaca. Namun, data deret waktu sering memiliki dimensi spasial. Oleh karena itu, dikembangkan model Spatial Vector Autoregressive (SpVAR) yang mempertimbangkan dimensi spasial dan waktu. Pembobot queen contiguity digunakan untuk representasi yang lebih akurat. Penelitian ini memanfaatkan data BPS DKI Jakarta dari Januari 2017 hingga Desember 2021. Hasilnya menunjukkan pengaruh spasial dalam model SpVAR (1,3) dengan bobot queen contiguity. Curah hujan, suhu, dan kelembaban udara saling mempengaruhi di wilayah diprediksi dan lainnya. Model ini penting dalam strategi mitigasi banjir dan kebijakan kota cerdas untuk mengurangi risiko banjir di DKI Jakarta.
 
Abstract
Climate change and extreme weather pose global challenges, including in Indonesia, leading to increased floods in DKI Jakarta. Addressing this requires accurate rainfall forecasts. The VAR model is used to understand the relationships between weather variables. However, time series data often have spatial dimensions. Therefore, a Spatial Vector Autoregressive (SpVAR) model has been developed considering both spatial and temporal dimensions. Queen contiguity weighting is used for more accurate representation. This study utilizes BPS DKI Jakarta data from January 2017 to December 2021. The results show spatial influence in the SpVAR (1,3) model with queen contiguity weighting. Rainfall, temperature, and humidity mutually influence predicted and other areas. This model is crucial for flood mitigation strategies and smart city policies to reduce flood risks in DKI Jakarta.
Arsitektur Sistem Percakapan Otomatis Berbahasa Indonesia dengan Normalisasi Bahasa Informal Menjadi Baku
Authors: Khairul Muhammad Fathur Rahman , Perdana Rizal Setya
Published: December 2023

Komunikasi merupakan hal yang paling penting dalam kehidupan sehari-hari. Setiap orang berkomunikasi dengan cara mereka berdasarkan latar belakang serta kedekatan antar pembicara. Oleh karena itu, perkembangan bahasa informal terjadi sangat cepat dan tidak jarang menciptakan kata-kata baru sebagai pengganti bahasa formal. Hal ini menjadi masalah jika dilihat dari perspektif pemrosesan bahasa alami (NLP). NLP umumnya hanya dapat dilakukan dengan bahasa yang formal dan tidak mampu menginterpretasikan makna dari kalimat informal. Maka dari itu, penulis mengusulkan pendekatan untuk memungkinkan mesin memahami bahasa informal dengan melakukan normalisasi bahasa infomal menjadi baku dengan memanfaatkan NLP. Pendekatan yang dilakukan akan melatih model pre-trained GPT-2 berbahasa Indonesia dengan data parallel corpus untuk memahami makna dari bahasa informal dan mampu menerjemahkannya ke dalam bentuk baku. Melalui eksperimen yang dilakukan, pendekatan ini mencapai tingkat akurasi 91% dan dapat menerjemahkan bahasa informal dengan baik. Performa ini dapat diraih dengan konfigurasi hiperparameter yaitu Adam optimizer dengan learning rate 1e-4, batch size sebesar 16 dan dropout rate sebesar 0,5.
 
Abstract
 
Communication is the most essential thing in daily life. Everyone communicates in their own way based on their background and the closeness between speakers. Thus, the development of informal language occurs quickly and it is often to create new words as a substitute for formal language. This is an issue from a natural language processing (NLP) perspective. NLP generally only works with formal language and is unable to interpret the meaning of informal sentences. Therefore, the authors propose an approach to enable machines to understand informal language by normalizing the informal language to standard by utilizing NLP. The approach will train a pre-trained GPT-2 model in Indonesian with parallel corpus data to understand the meaning of informal language and be able to translate it into standardized form. Through experiments, the method achieved 91% accuracy and can translate informal language well. This performance can be achieved with a hyperparameter configuration, namely Adam optimizer with a learning rate of 1e-4, batch size of 16 and dropout rate of 0.5.
Analisis Interaksi Aktivitas Pembelajaran Daring Berdasarkan Data Log Aktivitas pada Learning Managemefnt System (LMS) Menggunakan Educational Process Mining
Authors: Anwar Novriani Dewi , Setiawan Nanang Yudi , Purnomo Welly
Published: December 2023

Perpaduan perkembangan teknologi di bidang pendidikan telah mengembangkan suatu inovasi yang disebut LMS (Learning Management System) yang dapat diakses dari mana saja dan kapan saja tanpa harus melakukan pertemuan secara fisik di kelas. Kegiatan interaksi pada LMS memiliki event log yang dapat diekstraksi untuk mengidentifikasi analisis menggunakan Educational Process Mining (EPM). Kami mengekstrak event log dari ELING, platform LMS yang disediakan oleh fakultas kami. Selanjutnya, kami melakukan Process Mining menggunakan metode Heuristic Miner dari tiga dosen kelas mata kuliah Data Warehouse yang berbeda. Analisis yang dilakukan yaitu melihat gambaran kegiatan pembelajaran yang diberikan oleh masing-masing dosen dan pendekatannya dalam mengarahkan mahasiswa untuk berinteraksi dengan modul materi pada ELING apakah sesuai urutan dalam Rencana Pembelajaran Semester (RPS). Kami memvariasikan dependency threshold untuk mengidentifikasi modul pembelajaran mana yang memiliki tingkat ketergantungan yang lebih tinggi antar event yang menjadi inti pembelajaran mata kuliah tersebut. Pada dependency threshold 0,6, jumlah kegiatan dari model yang ditemukan untuk semua dosen adalah 21. Sementara pada threshold 0,8, jumlah kegiatan bervariasi antara 12 dan 20. Dengan variasi dependency threshold ini juga akan mengidentifikasi modul mana yang tetap muncul yang berhubungan dengan bentuk model aktivitas pembelajaran dan apakah model yang dihasilkan sesuai dengan urutan RPS.
 
Abstract
 
The combination of technological developments in the field of education has developed an innovation called LMS (Learning Management System) that can be accessed from anywhere and anytime without having to conduct physical meetings in class. Interaction activities in the LMS have event logs that can be extracted to identify analysis using Educational Process Mining (EPM). We extract event logs from ELING, an LMS platform provided by our faculty. Next, we conducted Process Mining using the Heuristic Miner method from three different Data Warehouse course class lecturers. The analysis carried out is to see an overview of the learning activities provided by each lecturer and their approach in directing students to interact with the material modules on ELING whether in the order in the Semester Learning Plan (RPS). We vary the dependency threshold to identify which learning modules have a higher level of dependency between events that are at the core of the course's learning. At a dependency threshold of 0.6, the number of activities from the model found for all lecturers was 21. While at the threshold of 0.8, the number of activities varies between 12 and 20. With dependency threshold variations, it will also identify which modules still appear that relate to the shape of the learning activity model and whether the resulting model matches the RPS sequence.
Klasifikasi Intensi dengan Metode Ling Short-Term Memory pada Chatbot Bahasa Indonesia
Authors: Al Farisi Faiz Aulia , Perdana Rizal Setya , Adikara Putra Pandu
Published: December 2023

E-government merupakan sebuah konsep pemerintahan yang menyelenggarakan layanan publik secara digital yang didukung oleh teknologi informasi, sehingga menjadi lebih prima. Salah satu bentuk layanan publik digital yang umum ada di berbagai sektor adalah helpdesk. Helpdesk memungkinkan pengguna dapat bertanya atau melaporkan sesuatu untuk kemudian dijawab oleh staf. Permasalahan yang ada adalah ketersediaan staf yang hanya dapat menjawab pada jam kerja, sedangkan pengguna bisa jadi menemukan permasalahan yang urgent di luar jam kerja. Oleh karena itu, solusi yang ditawarkan adalah penerapan chatbot, sehingga dapat melayani kapan pun, sekaligus meringankan kerja dari staf. Penelitian ini mencoba untuk merancang salah satu komponen pada chatbot, yaitu model klasifikasi intensi, dengan metode Long Short-Term Memory. Data yang digunakan merupakan 501 teks riwayat pertanyaan dari database Helpdesk TIK UB yang termasuk ke dalam 7 kelas intensi yang ditentukan. Data akan melalui beberapa tahap prapemrosesan sebelum kemudian dilakukan pemodelan dan beberapa pengujian. Tahap pengujian meliputi pemilihan embedding yang digunakan, pemilihan teknik augmentasi data, dan penyetelan hyperparameter. Hasil dari keseluruhan pengujian, didapatkan model terbaik yang mampu menghasilkan akurasi sempurna untuk data latih dan data uji, serta loss 0,004 untuk data latih dan 0,044 untuk data uji.
 
Abstract
 
E-government is a government concept that organizes digital public services supported by information technology, so that they become more excellent. One form of digital public service that is common in various sectors is the helpdesk. Helpdesk allows users to ask questions or report something to be answered by staff. The problem that exists is the availability of staff who can only answer during working hours, while users may find urgent problems outside of working hours. Therefore, the solution offered is the implementation of a chatbot, so that it can serve at any time, while easing the work of staff. This study attempts to design one of the components of the chatbot, namely the intention classification model, using the Long Short-Term Memory method. The data used are 501 question history texts from the Helpdesk TIK UB database which fall into the 7 specified intensity classes. The data will go through several pre-processing stages before then being modeled and tested. The testing phase includes selecting the embedding used, data augmentation techniques selection, and hyperparameter tuning. The best model is obtained which can produce perfect accuracy for training data and test data, as well as a loss of 0.004 for training data and 0.044 for test data.
Klasifikasi Tingkat Stress dari Data Berbentuk Teks dengan Menggunakan Algoritma Support Vector Machine (SVM) dan Random Forest
Authors: Fathirachman Mahing Naufal , Lazuardi Gunawan Alifi , Foresta Azhar Zen Ahmad , Abdurrachman Bachtiar Fitra , Agung Wicaksono Satrio
Published: December 2023

Stres merupakan keadaan dimana seseorang merasakan adanya tekanan yang berlebih pada dirinya. Pemantauan tingkat stres menjadi hal yang penting bagi manusia. Tingkat stres yang tinggi dapat menimbulkan dampak negatif terhadap kesehatan manusia. Deteksi dini stres menjadi sesuatu yang sangat penting untuk dilakukan. Salah satu cara mengetahui tingkat stres seseorang adalah melalui analisis teks. Penelitian ini dilakukan untuk melakukan klasifikasi tingkat stres berdasarkan data berupa teks menggunakan algoritma Support Vector Machine (SVM) dan Random Forest. Pada penelitian ini melakukan perbandingan beberapa metode transformasi. Transformasi yang dilakukan pada penelitian ini menggunakan TF-IDF, CountVectorizer, NRCLex, dan Word Affect Intensities. Data yang digunakan dalam penelitian ini berupa sebuat teks berbahasa Inggris yang diambil dari media sosial Twitter. Total data yang digunakan yaitu 8439 data. Pelatihan model baik untuk Support Vector Machine dan Random Forest menggunakan 6751 data. Sedangkan untuk pengujian menggunakan 1688 data. Hasil penelitian menunjukkan bahwa algoritma SVM dengan pembobotan menggunakan TF-IDF memiliki performa yang paling baik dibandingkan dengan algoritma Random Forest dan metode transformasi lainnya yang digunakan dalam penelitian. Model algoritma SVM dengan transformasi TF-IDF yang dibangun berhasil mendapatkan akurasi sebesar 84%. Model ini mendapatkan akurasi yang lebih tinggi dibanding model Random Forest yang memperoleh akurasi tinggi sebesar 80% dengan menggunakan transformasi CountVectorizer.
 
Abstract
Stress is a condition where a person feels excessive pressure on himself. Monitoring stress levels is important for humans. High levels of stress can have a negative impact on human health. Early detection of stress is something that is very important to do. One way to find out someone's stress level is through text analysis.This research was conducted to classify stress levels based on text data using the Support Vector Machine (SVM) and Random Forest algorithms. This research compares several transformation methods. The transformation performed in this study uses TF-IDF, CountVectorizer, NRCLex, and Word Affect Intensities. The data used in this research is an English text taken from Twitter social media. The total data used is 8439 data. Model training for both Support Vector Machine and Random Forest uses 6751 data. While for testing using 1688 data. The results showed that the SVM algorithm with weighting using TF-IDF had the best performance compared to the Random Forest algorithm and other transformation methods used in the study. The SVM algorithm model with TF-IDF transformation that was built managed to get an accuracy of 84%. This model obtained a higher accuracy than the Random Forest model which obtained a high accuracy of 80% using the CountVectorizer transformation.
Deteksi Land Surface Temperature Menggunakan Citra Landsat: Studi Kasus Kota Jababeka dan Sekitarnya
Authors: Zaky Abdul Rohman , Suhendra Suhendra , Arwin Muhammad Ayub
Published: December 2023

Land Surface Temperature (LST) merupakan salah satu parameter yang dapat digunakan untuk melihat dampak perubahan iklim dan lingkungan akibat perubahan tutupan dan alih fungsi lahan.  Studi kasus dilakukan di Kota Jababeka dan sekitarnya sebagai Area Of Interest (AOI) untuk mendapatkan informasi pola distribusi LST secara spasial dan temporal. Dalam kajian ini LST didapatkan dengan memanfaatkan dan mengolah thermal band pada citra Landsat multi temporal. Land Surface Emisivity (LSE) diturunkan dari NDVI (Normalized Difference Vegetation Index) dan proporsi vegetasi (PV). Nilai LSE digunakan sebagai salah satu parameter untuk menghitung LST dari citra Landsat.  Hasil kajian didapatkan bahwa kenaikan nilai rata-rata LST sebesar 3.03 ºC antara tahun 1990 dengan tahun 2000, dan terjadi penurunan nilai rata-rata LST berturut-turut pada interval periode selanjutnya yaitu sebesar 2.39 ºC antara tahun 2000 dengan tahun 2010 dan sebesar 0.54 ºC antara tahun 2010 dengan tahun 2020. Kondisi ini dipicu oleh adanya kenaikan luasan atas kerapatan vegetasi sekitar 24% dari luas daerah penelitian sepanjang periode kajian yang diketahui berdasarkan distribusi spasial NDVI dan LSE. Hasil kajian ini diharapkan dapat menjadi pertimbangan dalam pengambilan keputusan dan menentukan arah kebijakan pengelolaan dan pengembangan Kota Jababeka dan sekitarnya di masa yang akan datang.
 
Abstract
Land Surface Temperature (LST) is one of parameters used to assess the impacts of climate and environmental changes resulting from changes in land cover and land use conversion. A case study was conducted in Kota Jababeka and its surrounding region as the Area Of Interest (AOI) to obtain information on the spatial and temporal distribution patterns of LST. In this study, LST was obtained by utilizing and processing the thermal band of Landsat multi-temporal images. Land Surface Emissivity (LSE) was derived from NDVI (Normalized Vegetation Index) and proportion of vegetation (PV). LSE value is used as one of parameter to calculate LST obtained from Landsat imagery. The study's findings revealed that the average LST increased by 3.03 ºC between 1990 and 2000, and subsequently, there were consecutive decreases in the average LST during the following time intervals: 2.39 ºC between 2000 and 2010 and 0.54 ºC between 2010 and 2020. These changes were triggered by an increase in vegetation cover of about 24% in the study area throughout the research period, as observed from the spatial distribution of NDVI and LSE. The results of this study are expected to be considered in decision-making and determining the direction of management and development in Kota Jababeka and its surrounding areas in the future.
Prediksi Detak Jantung Berbasis LSTM pada Raspberry Pi untuk Pemantauan Kesehatan Portabel
Authors: Zen Ahmad Foresta Azhar , Pramukantoro Eko Sakti , Amron Kasyful , Wardhani Viera , Kamila Putri Annisa
Published: December 2023

Penyakit kardiovaskular atau cardiovascular disease (CVD) menduduki peringkat teratas penyebab kematian di dunia. Diperkirakan sekitar 17,9 juta jiwa meninggal akibat CVD pada tahun 2019, yang menyumbang sebanyak 32% dari seluruh kematian global. Penting untuk mendeteksi kelainan pada jantung sedini mungkin untuk mencegah kematian karena CVD. Peningkatan kesadaran tentang pentingnya pemantauan kesehatan diri sendiri telah mendorong perkembangan teknologi pemantauan kesehatan portabel. Dalam penelitian ini, kami mengusulkan model prediksi detak jantung berbasis Long Short-Term Memory (LSTM) dengan menggunakan fitur RR-Interval dan mengimplementasikan pada perangkat Raspberry Pi. Model berbasis LSTM merupakan salah satu jenis arsitektur jaringan saraf tiruan yang mampu menangani data berurutan dengan baik, sehingga sangat cocok untuk pemantauan dan prediksi detak jantung yang bersifat sekuensial. Raspberry Pi dikenal karena ukurannya yang kecil, harga yang terjangkau, kinerja yang andal, dan efisiensi komputasi yang baik. Raspberry Pi juga memungkinkan integrasi yang mudah dengan berbagai sensor, menjadikannya solusi yang cocok untuk pemantauan kesehatan yang portabel. Hasil penelitian ini menunjukkan bahwa model klasifikasi yang diusulkan memiliki kinerja yang baik dengan tingkat akurasi mencapai 96,66%. Implementasi inferensi pada Raspberry Pi juga menunjukkan performa yang baik, dengan waktu 4,82 detik untuk melakukan inferensi data sepanjang 100 detik, serta penggunaan memori sebesar 134,8MB.
 
Abstract
Cardiovascular diseases (CVDs) rank as the top cause of global death. An estimated 17.9 million people succumbed to CVDs in 2019, constituting 32% of all global deaths. Detecting heart abnormalities as early as possible is crucial to prevent CVD-related fatalities. The growing awareness of the importance of self-health monitoring has driven the development of portable health monitoring technologies. In this study, we propose a Long Short-Term Memory (LSTM)-based heart beat prediction model using RR-Interval as features  and implement it on the Raspberry Pi device. LSTM models are a type of artificial neural network architecture known for their ability to handle sequential data effectively, making them highly suitable for sequential heart rate monitoring and prediction. The Raspberry Pi is renowned for its compact size, affordability, reliable performance, and efficient computational capabilities. It also enables seamless integration with various sensors, making it an ideal solution for portable health monitoring. This research show that the proposed classification model performs well, achieving an accuracy rate of 96.66%. The implementation of inference on the Raspberry Pi also demonstrates good performance, with an average inference time of 4.82 seconds for processing 100 data points and a memory usage of 134.8MB.
Implementasi Teknologi Blockchain dengan Sistem Smart Contract pada Klaim Asuransi
Authors: Baihaqsani  , Kusyanti Ari , Trisnawan Primantara Hari
Published: December 2023

Perkembangan sistem klaim pada asuransi di Indonesia umumnya masih menerapkan pengajuan klaim dengan menggunakan sistem secara manual dengan metode cashless dan reimbursement. Penerapan sistem klaim pada asuransi tersebut memiliki proses administrasi yang cukup panjang sehingga dapat memakan waktu yang lama dan penerapan sistem tersebut tidak dapat memberikan proses tranparansi transaksi pada klaim asuransi. Dengan permasalahan tersebut dapat menimbulkan permasalahan terkait dengan keamanan informasi berupa integritas data, kerahasiaan data, dan transparansi data pada klaim asuransi. Penelitian ini mengusulkan pengembangan sistem klaim pada asuransi yang dapat dilakukan secara online dengan menerapkan teknologi blockchain. Teknologi blockchain menggunakan sistem penyimpanan data dengan menerapkan sistem desentralisasi aplikasi yang berfungsi untuk memberikan kendali penuh terhadap pengguna atas data mereka dengan tidak adanya perantara terpusat. Dengan menerapkan desentralisasi aplikasi tersebut dapat diwujudkan integritas, kerahasiaan dan transparansi transaksi pada klaim asuransi. Penelitian ini mengajukan usulan implementasi teknologi blockchain dengan sistem smart contract pada proses klaim asuransi untuk dijadikan solusi terhadap masalah yang terdapat pada proses klaim asuransi.
 
Abstract
The development of the insurance claim system in Indonesia generally still applies the submission of claims using a manual system with cashless and reimbursement methods. The application of the insurance claim system has a fairly long administrative process that can take a long time and the application of the system cannot provide a transparent transaction process for insurance claims. With these problems can cause problems related to information security in the form of data integrity, data confidentiality, and data transparency on insurance claims. This research proposes the development of an insurance claim system that can be done online by applying blockchain technology. Blockchain technology uses a data storage system by implementing a decentralized application system that functions to give users full control over their data in the absence of a centralized intermediary. By implementing a decentralized application, integrity, confidentiality and transaction transparency in insurance claims can be realized. This study proposes the implementation of blockchain technology with a smart contract system in the insurance claim process to be used as a solution to problems in the insurance claim process
Analisis Forensik Digital untuk Investigasi Kasus Cyberbullying pada Media Sosial Tiktok
Authors: Mikayla Halimah Septya , Kusyanti Ari , Trisnawan Primantara Hari
Published: December 2023

TikTok merupakan media sosial yang populer digunakan pada masa kini. Media sosial TikTok yang populer di kalangan pengguna menjadi salah satu media yang banyak ditemui jenis kejahatan siber cyberbullying. Kasus cyberbullying pada media sosial TikTok dapat ditindak secara hukum yakni dengan dilakukan investigasi forensik digital. Penelitian ini dilakukan untuk mengumpulkan dan menganalisis bukti digital kasus cyberbullying pada TikTok android dan juga TikTok web dengan melakukan skenario kasus serta menerapkan model investigasi forensik digital yang berfokus pada jejaring sosial. Fase skenario terdiri dari persiapan, perancangan, serta pelaksanaan. Data dari skenario ini kemudian dilakukan forensik digital fase dengan tahapan-tahapan berikut: planning, reconnaissance, collection, transport, examination, identification, analysis, classification, reporting, dan presentation. Penelitian ini berhasil mendapatkan bukti-bukti digital untuk membuktikan kasus cyberbullying yang dieksperimenkan dengan skenario serta dengan model investigasi forensik digital yang diterapkan. Didapatkan hasil perbandingan yang signifikan pada perbedaan antara bukti digital TikTok android dan TikTok web. Secara keseluruhan, penelitian ini berhasil memperoleh barang bukti digital dengan persentase sebesar 68,8% dari perbandingan data awal skenario dengan data ditemukan dari hasil forensik digital.
 
Abstract
TikTok is currently a widely popular social media platform among users and is also a media where various forms of cyberbullying are encountered. Cases of cyberbullying on TikTok can be subject to legal prosecution through digital forensic investigations. This research aims to collect and analyze digital evidence related to cyberbullying cases on TikTok's Android and Website platforms. It involves the creation of case scenarios and the application of a digital forensic investigative model specifically focused on social networks. The scenario phase encompasses preparation, design, and implementation. Data obtained from these scenarios is subsequently subjected to a digital forensics phase, consisting of these stages: planning, reconnaissance, collection, transport, examination, identification, analysis, classification, reporting, and presentation. This research successfully obtained digital evidence that substantiates cases of cyberbullying, as simulated in the scenarios and investigated using the applied digital forensic model. Significant differences were observed in the digital evidence between TikTok on Android and TikTok on the web. In summary, this study achieved a 68.8% match between the initial scenario data and the data retrieved through digital forensics, ultimately obtaining valuable digital evidence.
New Transformations of the public Sphere: Contemporary Discussions in Germany
Authors: Mikhaylov Igor
Published: December 2023

The article discusses the main stages and approaches to analyzing the problem of publicity using the example of the works of Heidegger, Horkheimer, and Adorno. The reasons for the absence of a positive concept of publicity in German philosophy of the first half of the twentieth century are shown, as well as the connection between a negative attitude towards publicity and the global socio-historical pessimism of that time. The significance of the theory of publicity presented in two studies of Habermas, “Structural Changes of the Public Sphere” (1962) and publications 2021–2022, is analyzed, as well as its connection with the political theory of democracy. Habermas interprets the “public sphere” as a special space for the application of critical discourse, emerging in the era of the emergence of capitalism. If in feudal society “publicity” is identified with the state, then in the 18th–19th centuries. a practice of discussions about literature is emerging, gradually expanding to a critical discussion of social processes. With the achievement by the middle of the twentieth century bourgeois society at the stage of “mass democracy” and the intervention of the state, which actively uses manipulative technologies, the rational foundations of the discourse of the public sphere give way to non-rational ones. The area of publicity becomes an area of confrontation and conflict between the interests of various social groups. New structural transformations in the sphere of publicity become noticeable in 2010–2020 and are associated with the emergence of new media, the new role of social networks. One of Habermas’ main critical arguments points out that the media structure changed by digitalization may deepen contemporary problems with contemporary Western democracy and result in deepening of it’s crisis. Habermas’ theory is contrasted against the theories of other media theorists (Marshall McLuhan, Niklas Luhmann and others).
Case Study: Enteral Nutrition in Stroke Patients with Nasogastric Tube at Undata Hospital
Authors: Pamolango Julio Alvero , Pettalolo Sri Rezeki , Toding Selly , Nurdiana Nurdiana , Imansari Adillah , Anggraini Anissa
Published: December 2023

The aim of this study was to determine the adequacy of commercial enteral nutrition for nutrition needs of stroke patients with Nasogastric Tube (NGT) installation at Undata Hospital. The research was a case study of one stroke patient with NGT. The patient's nutrient intake for three days was measured for energy requirement (87.6%), carbohydrates (76.7%), protein (83.76%), and fat (122.98%). The patient was given commercial food Blendera-MF and Virgin Coconut Oil (VCO) in moderation without incidence of vomiting. Therefore, giving enteral feeding (Blendera-MF) to patients fulfills their nutritional needs.
Relationships of Screen Time and Sugar-Sweetened Beverages Consumption with Body Fat Percentage in Female Students in Semarang
Authors: Setyawati Nanda , Ulvie Yuliana N.S. , Sulistiani Ria P. , F.J. Firdananda
Published: December 2023

This study aimed to determine the relationship between screen time duration and Sugar-Sweetened Beverage (SSB) consumption with body fat percentage among female students in Semarang. This cross-sectional study employed 75 female students aged 18‒25 years in Islamic Boarding School KH Sahlan Rosjidi Semarang. The mean screen time duration was 8.41±3.28 hours/day, SSB consumption frequency was 4.77±4.31 times/week, and body fat percentage was 26.76±7.26%. SSB consumption was dominated by powdered drinks. The test has discovered a relationship between screen time duration (p=0.000, r=0.443) and the frequency of SSB consumption such as powdered drinks (p=0.002, r=0.350), packaged milk (p=0.038, r=0.240), yogurt (p=0.013, r=0.286), packaged coffee (p=0.022, r=0.264), and carbonated drinks (p=0.005, r=0.320) with body fat percentage.
Association between Pre-Conception Health and Nutrition Knowledge- Attitude-Practice and Anemia among Pre-Marital Women in Sleman, Indonesia
Authors: Ansari Muhammad Ridwan , Putri Febriani , Nangley Wulan , Kandarina Bernadette Josephine Istiti
Published: December 2023

The study aimed to assess the association between pre-conception health and nutrition Knowledge-Attitude-Practice (KAP) and anemia among Pre-Marital Women (PMW). In this cross-sectional study, Mid-Upper Arm Circumference (MUAC) measurement, 24-hour food recall interview, blood sample collection, and validated KAP questionnaire interview were conducted to 111 PMW in Sleman in 2021. The study found that 21.6%, 14.4%, and 49.1% of PMW had Chronic Energy Deficiency (CED), anemia, and lack of minimum dietary diversity, respectively. The proportions of PMW with adequate KAP were 70.5%, 67.8%, and 64.3%, respectively. Pre-conception health and nutrition practices are associated with anemia (p<0.05), but knowledge and attitude are not.
Development of Iron- and Vitamin A-Rich Dim Sum from Gourami (Osphronemus goramy) and Chicken Liver for Women of Reproductive Age
Authors: Putri Adinda Sekar Damar , Aries Muhammad , Nasution Zuraidah
Published: December 2023

This study aims to develop and investigate the nutritional value of gourami dim sum products with chicken liver substitution. The research phase includes formulation and nutrient content analysis. Based on the results, one serving of selected formula of gourami dim sum can provide 6.4% to 7.1% of the energy requirement. The dim sum can be claimed as a low in fat and high in iron and vitamin A product.
Functional Properties of Soy Yoghurt with Red Dragon Fruit Substitution
Authors: Marjan Avliya Quratul , Mustika Nurul , Fatmawati Iin , Arini Firlia Ayu
Published: December 2023

This study investigated the antioxidant activity and lactic acid bacteria content of soy yoghurt with red dragon fruit substitution. Analysis included sensory evaluation, proximate analysis, quantification of antioxidant activity and estimation of total Lactic Acid Bacteria (LAB). Formula 3 with 35% red dragon fruit substitution was selected as the optimal formula. Proximate analysis showed water content of 92.75%, ash content of 0.23%, protein content of 0.48%, fat content of 3.08%, and carbohydrate content of 3.46%. The selected product had an increased antioxidant activity. In conclusion, soyghurt with red dragon fruit can increase the antioxidant activity of the product.
Quality of Chicken Sausage during Storage with Addition of Andaliman Fruit Powder
Authors: Julianti Elisa , Lubis Zulkifli , Hilman Adrian
Published: December 2023

The study aimed to evaluate the impact of Andaliman Fruit Powder (AFP) concentrations ranging from 0.5 to 1.5% on the quality of chicken sausages over a 4-week storage period. The result showed that a higher concentration of AFP had a significant (p<0.05) increase in total phenolics and total flavonoids, while the total microbes and free fatty acid content decreased. There were no significant differences (p>0.05) observed in the protein content, pH, texture, and panelists' acceptance of color, aroma, and taste in comparison with the control. The incorporation of 1.5% AFP can be applied to maintain the quality of chicken sausage during storage at 10°C.
Physicochemical Properties and Stability of Emulsions Containing Carotenoid-Rich Pumpkin Extract, Whey Protein Concentrate and Carboxymethylcellulose
Authors: Ibrahim Nor Hayati , Yi Tan Kang , Isa Suaidah Mohd , Hadi Nabilah Abdul , Ali Asma’
Published: December 2023

A response surface methodology with a central composite design was employed to investigate the effects of Carotenoid-Rich Pumpkin Extract (CE), Whey Protein Concentrate (WPC), and Carboxymethyl Cellulose (CMC) on the physicochemical properties and stability of emulsions. The WPC-CMC synergism resulted in the formation of a thick layer around the droplets, thus improving the emulsion system. The optimum levels of CE, WPC, and CMC were determined to be 6.00, 2.00, and 0.87%, respectively, to provide the desired emulsion with a pH of 4.18, a carotenoid degradation stability index of 0.88, a peroxide formation stability index of 0.77, and an apparent viscosity of 0.61 Pa.s.
Properties of an Emulsion Gel with Gracilaria fisheri Agar, Whey Protein Isolate and Black Seed Oil
Authors: Omar Noorain Nasuha , Ibrahim Nor Hayati , Mohamad Nizaha Juhaida , Achudan Shamini Nair , Amin Amiza Mat
Published: December 2023

Gracilaria fisheri agar has beneficial potential for human health; however, its application in food products needs to be incorporated into an emulsion gel due to its low gel strength. A response surface methodology with a central composite design was utilized to prepare emulsion gels containing 4–6% Gracilaria fisheri Agar (AGF), 4–8% Whey Protein Isolate (WPI) and 10–20% Black Seed Oil (BSO). The optimum levels of AGF, WPI, and BSO were determined to be 6%, 4% and 10% respectively to give the preferred gel strength of 966.24 g, water holding capacity of 96.12%, and DPPH antioxidant activity of 59.16%.
L’entretien de recherche avec des journalistes. Propos introductifs
Authors: Broustau Nadège , Jeanne-Perrier Valérie , Le Cam Florence , Pereira Fabio Henrique
Published: December 2023

Introduction au numéro.
Des journalistes enfin libérés de leurs sources ? Promesse et réalité du "journalisme de données "
Authors: Parasie Sylvain , Dagiral Eric
Published: December 2023

Depuis plusieurs années, le traitement de données apparaît à certains journalistes comme une façon de réduire leur dépendance aux sources. En élaborant des bases de données et des algorithmes au sein des rédactions, ils seraient moins tributaires des tuyaux et des interprétations que leur fournissent régulièrement leurs sources, notamment institutionnelles. S’inspirant de la sociologie pragmatique et de la théorie de l’acteur-réseau, cet submission montre que les technologies de traitement de données sont actuellement mobilisées afin de permettre aux journalistes de reprendre l’initiative face aux sources. À partir d’une enquête de terrain menée à Chicago en 2010, nous examinons d’abord le contenu de cette promesse apparue au croisement des mondes journalistiques et informatiques. Puis, en étudiant le cas de l’information criminelle à Chicago, nous montrons que cette promesse n’est qu’en partie satisfaite dans les réalisations actuelles.

 

Over the past few years, some journalists have expressed that data processing is a way of reducing their dependence to sources. Elaborating databases and algorithms in the newsrooms is considered to reduce journalists’ need for tips and interpretations from sources, including those institutional. By drawing from pragmatic sociology and the network-actor theory, this submission shows that data-processing technologies are currently mobilized to encourage journalists to reinitiate their relation to sources. Based on a study that took place in Chicago in 2010, first we examine the content of the promise that appeared at the intersection of the journalistic and computer science worlds. Then, by analysing criminal news in Chicago, we show that currently, the promise is only partly fulfilled.

 

Há vários anos que alguns jornalistas acreditam que o processamento de dados pode ser uma forma de reduzir sua dependência em relação às fontes de informação. Ao desenvolverem bases de dados e algoritmos no interior das redações, eles seriam menos dependentes das dicas e interpretações fornecidas regularmente pelas fontes, sobretudo as institucionais. Tendo como inspiração a sociologia pragmática e a teoria do ator-rede, este artigo mostra como as tecnologias de tratamento de dados são atualmente mobilizadas para permitir que os jornalistas possam assumir novamente a iniciativa frente às fontes. A partir de uma pesquisa de campo realizada em Chicago, em 2000, examinamos, em um primeiro momento, o conteúdo dessa promessa, advinda do cruzamento dos mundos do jornalismo e da informática. Em seguida, por meio de um estudo de caso sobre a informação criminal em Chicago, nós mostramos que essa promessa não foi totalmente cumprida.
Assessment of different fertilization sources for improving zinc bioavailability in wheat under saline conditions
Authors:   Institute of Soil and Environmental Sciences, University of Agriculture, Faisalabad-38040, Pakistan, Ali Tasawar , Anwar-ul-Haq Muhammad ,   Institute of Soil and Environmental Sciences, University of Agriculture, Faisalabad-38040, Pakistan, Akhtar Javaid ,   Institute of Soil and Environmental Sciences, University of Agriculture, Faisalabad-38040, Pakistan, Waraich Ejaz Ahmad ,   Department of Agronomy, University of Agriculture, Faisalabad-38040, Pakistan
Published: December 2023

In modern agriculture, soil salinity stress has been extensively studied by researchers owing to its disastrous impacts on soil and plant health. Zinc (Zn) is an essential micronutrient. Crucial role of Zn in regulating plant growth under salinity stress has been well documented. Current research consisted of two studies, first study was aimed at comparing three organic (i.e. sugarcane filter cake, farmyard manure and poultry manure) and inorganic sources (i.e. zinc oxide, zinc sulphate and zinc nitrate) of Zn for improving Zn uptake by salt tolerant wheat varieties in a pot trial. Whereas, in second experiment the effect of zinc sulfate (ZnSO4; inorganic) alone and in combination with sugarcane filter cake (SFC; organic) was evaluated in sustaining wheat growth under naturally salt affected soil. Results from pot trial suggested that among different organic sources, SFC application significantly enhanced the plant height (12.52%), spike length (9.07%) and number of fertile tillers (25%), whereas, application of ZnSO4 among the inorganic sources resulted in notable enhancements in plant height (7%), number of fertile tillers (24%), and spike length (8%) compared to their corresponding treatments. In addition, wheat variety Sehar-2006 proved to be the most efficient wheat variety under salinized conditions. Whereas the findings of the field trial revealed ZnSO4 application @ 24 kg ha-1 in combination with SFC 1 % w/w improved the plant height (2%), grain yield (81%), K+/Na+ ratio (1.4 fold), catalase (8%) and peroxidase (3%) activities.
Non-invasive vital teeth whitening for enhanced facial esthetic: A case report
Authors: Chauhan Megna , Singh Kriti , Chauhan Raju , Shukla Preeti , Misra Pradyumna
Published: December 2023

For ages, a bright white smile symbolizes beauty, wellness, and vitality. The translucency and thickness of the enamel, hue of the underlying dentin, vitality, and color of the pulp all contribute to the pigmentation of teeth. Color changes in the crown can be due to physiologic or pathologic and endogenous or exogenous factors. Extrinsic or intrinsic stains are the most common causes of tooth discoloration. Discoloration, especially in the anterior teeth, seriously impairs esthetics and might make a patient feel less confident. In the treatment of discolored teeth, various invasive and non-invasive options, such as veneers and bleaching, are available, respectively. However, the urge for esthetic dentistry, particularly teeth whitening, has grown significantly in recent decades. As compared to invasive treatment options, non-invasive methods like bleaching (teeth whitening) are the most conservative choice for stained teeth and are preferred in situations where tooth structure is intact. Teeth whitening has developed into a well-liked and commonly requested dental procedure as a result of the public need for a brighter smile and enhanced looks. This paper aims to describe a case of vital, discolored anterior teeth due to fluorosis lightened using 35% hydrogen peroxide. A 19-year-old female patient reported to the department with discolored upper front teeth. After the procedure was completed, a pleasing esthetic result was achieved.