bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deteksi Objek menggunakan Dashboard Camera untuk Sistem Peringatan Pencegah Kecelakaan pada Mobil
Authors: Sutjiadi Raymond , Pattiasina Timothy John 
Published: June 2020

Saat ini penggunaan dashboard camera marak digunakan pada mobil untuk merekam kondisi sekitar kendaraan ketika berkendara. Dashboard camera adalah semacam kamera yang ditempatkan pada bagian dashboard mobil dengan kamera menyorot ke arah depan kendaraan yang berfungsi untuk merekam kondisi jalan. Di lain pihak, pada mobil premium saat ini sudah disematkan beberapa teknologi canggih untuk mencegah terjadinya kecelakaan atau tabrakan yang biasa disebut dengan Forward Collision Warning System. Teknologi ini pada dasarnya berfungsi untuk mencegah terjadinya tabrakan dari arah depan, baik dengan cara aktif ataupun pasif. Pada penelitian ini akan dibuat sebuah sistem terintegrasi dimana dashboard camera, yang diimplementasikan menggunakan kamera smartphone berbasis Android, tidak hanya digunakan untuk perekaman secara statis, tetapi juga digunakan untuk membuat sistem pencegah kecelakaan secara pasif. Adapun aplikasi ini dibuat dengan menggunakan metode pengolahan citra digital untuk mendeteksi keberadaan objek di depan mobil dengan menggunakan Tensorflow Open Source Machine Learning Library. Dari hasil pengujian tampak bahwa aplikasi ini mampu mendeteksi objek kendaraan berupa mobil penumpang, bus, dan truk, serta dapat memberikan peringatan baik secara visual maupun alarm apabila kendaraan di depan sudah berada pada jarak yang cukup dekat untuk memperingatkan pengemudi akan bahaya tabrakan. AbstractNowadays dashboard camera becomes familiar to be used in a car to record the condition around the vehicle while driving. Dashboard camera is a video camera placed in car’s dashboard faces in front of the vehicle to record the road condition. In the other side, premium cars now equipped with advanced technology to prevent collision called Forward Collision Warning System. This technology basically acts to prevent front collision, either in active or passive ways. In this research was built integrated system where dashboard camera, which implemented by camera of Android based smartphone, not only used as static recording, but also as passive collision avoidance system. This application was developed using Object Detection Method in Tensorflow Open Source Machine Learning Library. The research stage was started from problem analysis, literature study to search comparison from previous research, also software development and finalized with testing to measure system performance. From the testing result, this application was able to detect vehicle objects in form of passenger car, bus, and truck, also could provide both visual and alarm warning when there was a vehicle come closely from in front, to warn the driver about the danger of collision.
Game Edukasi Pengenalan Hewan Langka Berbasis Android Menggunakan Construct 2
Authors: Damayanti Damayanti , Akbar Muhammad Fadil , Sulistiani Heni 
Published: June 2020

Negara Indonesia memiliki kepulauan yang sangat luas dari daratan sampai pegunungan. Wilayah dalam kepulauan Indonesia terdapat flora dan fauna serta mikroba yang bermacam-macam. Habitat hewan di Indonesia mulai mengalami kepunahan, ini terjadi karena rusaknya habitat tempat tinggal hewan tersebut. Seiring dengan kepunahan hewan langka di Indonesia dan minimnya pengetahuan anak akan hewan-hewan langka khas daerah Indonesia serta kurangnya media pengenalan hewan langka. Maka penting dibuat game edukasi pengenalan hewan langka berbasis Android agar pengetahuan anak-anak dan masyarakat tentang hewan langka semakin meningkat. Penelitian ini menggunakan metode pengembangan sistem agile dan model UML (Unified Modelling Language). Tujuan dalam penelitian ini yaitu membangun sebuah game edukasi pengenalan hewan langka berbasis android yang memberikan edukasi sambil bermain dan dapat diakses melalui smartphone. Pembuatan game edukasi menggunakan construct2 menjadi lebih mudah karena memiliki tools yang khusus dirancang untuk pembuatan game. Berdasarkan pengujian aplikasi game edukasi pengenalan hewan langka ini dapat membantu memperkenalkan hewan langka kepada masyarakat khususnya pada anak-anak, dengan perolehan presentasi penilaian pengujian sebesar 93,21%. AbstractThe country of Indonesia has very wide islands from land to mountains. Areas in the Indonesian archipelago have diverse flora and fauna and microbes. Animal habitats in Indonesia are beginning to become extinct, this occurs because of the destruction of the habitat where the animals live. Along with the extinction of rare animals in Indonesia and the lack of knowledge of children about rare animals typical of the Indonesian region and the lack of media for the introduction of rare animals. So it is important to make an Android-based rare animal recognition education game so that children's knowledge and society about endangered animals increases. This study uses the agile system development method and the UML (Unified Modeling Language) model. The purpose of this study is to build an educational game for the introduction of rare animals based on android that provides education while playing and can be accessed via a smartphone. Making educational games using construct2 is easier because it has tools specifically designed for game creation. Based on the testing of this rare animal recognition educational game application, it can help introduce rare animals to the public, especially to children, with the acquisition of test rating presentations at 93.21%.
Pengembangan Sistem Penilaian Kematangan Tandan Buah Segar Kelapa Sawit menggunakan Citra 680 dan 750 NM
Authors: Setiawan Agung Wahyu , Ananda Alfie R. 
Published: June 2020

Salah satu permasalahan utama dalam industri kelapa sawit adalah proses sortasi Tandan Buah Segar (TBS) di pabrik kelapa sawit. Parameter yang digunakan dalam sortasi TBS adalah jumlah brondolan kelapa sawit. Pada saat ini, sortasi dilakukan oleh grader yang bersifat subyektif dan sering kali tidak konsisten. Hal ini terjadi karena keterbatasan penglihatan dan kemampuan manusia untuk mengolah informasi jumlah brondolan setiap TBS dalam waktu yang terbatas. Oleh karena itu, pada penelitian ini dikembangkan sistem penilaian kematangan TBS kelapa sawit berbasis spektroskopi dan nilai kontras citras. Sumber cahaya yang digunakan pada penelitian ini adalah lampu berjenis Light-emitting Diode (LED) dengan panjang gelombang 680 dan 750 nm. Akuisisi citra TBS dilakukan dengan menggunakan kamera DSLR yang telah dimodifikasi. sehingga diperoleh dua citra TBS pada panjang gelombang 680 dan 750 nm. Kemudian, dilakukan perhitungan nilai kontras kedua citra tersebut. Dalam penelitian ini, terdapat 24 TBS yang digunakan sebagai data latih, dengan komposisi 10 TBS matang dan 14 TBS mentah. Data uji yang digunakan berjumlah 77 TBS yang terdiri dari 38 matang dan 39 mentah. Pada penelitian ini, Support Vector Machine (SVM) digunakan sebagai metode klasifikasi. Akurasi data latih yang diperoleh adalah 66,67%. Sedangkan akurasi data uji dari sistem yang dikembangkan dalam penelitian ini adalah 57,14%. Hasil yang diperoleh ini masih perlu diperbaiki untuk meningkatkan akurasi sistem dengan cara menambah jumlah data, baik data latih maupun uji, serta menggunakan pembelajaran mesin. AbstractOne of the main problems in the palm oil industry is the grading of Fresh Fruit Bunches (FFB) in the palm oil mills. The parameter used for the process is the number of fruitlets detached from the bunch. Nowadays, the FFB grading is conducted by graders which is subjective and often inconsistent due to the limitation of human vision and ability to process information on the number of fruitlets detached per FFB in a very limited time. Therefore, this study developed a grading system to assess and estimate the FFB maturity based on spectroscopy and image contrast value. From the literature review, visible light and NIR spectrum in 680 and 780 nm can be used as light sources to detect the maturity level of FFB. DSLR camera is used to acquire the FFB image. Using this scheme, two FFB images in 680 and 750 nm are obtained. The next process is to calculate the image contrast. In this research, there are 24 FFB that are used as training data that consists of 10 ripe and 14 unripe. A total of 77 FFB are used as test data that consists of 38 ripe and 39 unripe. Support Vector Machine (SVM) is used in this research to classify the maturity level of FFB. The accuracy of the training dataset is 66.67%. Meanwhile, the accuracy of the test data is 57.14%. Future works will focus on enhancing accuracy of the system through increasing the number of training and testing data using machine learning.
Komparasi Data Mining Naive Bayes dan Neural Network memprediksi Masa Studi Mahasiswa S1
Authors: Azahari Azahari , Yulindawati Yulindawati , Rosita Dewi , Mallala Syamsuddin 
Published: June 2020

Prediksi kelulusan dibutuhkan oleh manajemen perguruan tinggi dalam menentukan kebijakan preventif terkait pencegahan dini kasus drop out. Lama masa studi setiap mahasiswa bisa disebabkan dengan berbagai faktor. Dengan menggunakan data mining algoritma naive bayes dan neural network dapat dilakukan prediksi kelulusan mahasiswa di STMIK Widya Cipta Dharma (WiCiDa) Samarinda . Atribut yang digunakan yaitu, umur saat masuk kuliah, klasifikasi kota asal Sekolah Menengah Atas, pekerjaan ayah, program studi, kelas, jumlah saudara, dan Indeks Prestasi Kumulatif (IPK). Sampel mahasiswa yang lulus dan drop-out pada tahun 2011 sampai 2019 dijadikan sebagai data training dan data testing. Sedangkan angkatan 2015–2018 digunakan sebagai data target yang akan diprediksi masa studinya. Sebanyak 3229 mahasiswa, 1769 sebagai data training, 321 sebagai data testing, dan 1139 sebagai data target. Semua data diambil dari data mahasiswa program strata 1, dan tidak mengikut sertakan data mahasiswa D3 dan alih jenjang/transfer. Dari data testing diperoleh tingkat akurasi hanya 57,63%. Hasil penelitian menunjukkan banyaknya kelemahan dari hasil prediksi naive bayes dikarenakan tingkat akurasi kevalidannya tergolong tidak terlalu tinggi. Sedangkan akurasi prediksi neural network adalah 72,58%, sehingga metode alternatif inilah yang lebih baik. Proses evaluasi dan analisis dilakukan untuk melihat dimana letak kesalahan dan kebenaran dalam hasil prediksi masa studi.AbstractGraduation predictions are required by the higher education institution preventive policies related to the early prevention of drop-out cases. The duration of study, for each student can be caused by various factors. By using the data mining algorithm Naive bayes and neural network, the student graduation in STMIK Widya Cipta Dharma (WiCiDa) can be predicted. The attributes used are as follows: age at admission, classification of cities from high school, father’s occupation, study program, class, number of siblings, and grade point average (GPA). Samples of students who graduated and dropped out between year 2011 and 2019 were used as training data and testing data. While the year class of 2015to 2018 is used as the target data, which will be predicted during the study period. According to the data mining algorithm Naive bayes, there are 3229 students; 1769 as training data, 321 as testing data, and 1139 as target data. All data is taken from students enrolled in undergraduate program and does not include data on diploma students and transfer student. From the testing data, an accuracy rate only 57.63%. The other side, prediction accuracy of the neural network is 72.58%, so this alternative method is the best chosen. The research results show the many weaknesses of the results of prediction of Naive bayes because the level of accuracy of its validity is not high. The evaluation and analysis process are conducted to see where the errors and truths are in the results of the study period predictions.
Knowledge Management System Berbasis Web tentang Budidaya Hidroponik untuk Mendukung Smart Society
Authors: Wardhana Ariq Cahya , Nurhadryani Yani , Wahjuni Sri 
Published: June 2020

Meningkatnya jumlah populasi penduduk di Indonesia berdampak pada terbatasnya luas wilayah pertanian di Kota Bogor yang mengakibatkan ancaman produksi pertanian karena konversi lahan persawahan sebesar 88,12% menjadi perumahan dan kebun. Solusi sistem produksi pertanian dengan terbatasnya lahan salah satunya adalah hidroponik. Untuk meningkatkan pengetahuan budidaya hidroponik dan memudahkan akses fasilitas belajar digital sebagai bagian penting dari rencana pemerintah Kota Bogor yaitu smart society diperlukan dukungan teknologi informasi. Penelitian ini bertujuan mengembangkan Knowledge Management System (KMS) budidaya hidroponik dengan mengadopsi metode Knowledge Management Life Cycle melalui identifikasi pengetahuan tacit maupun explicit dari komunitas hidroponik. Proses menangkap pengetahuan berorientasi pada seluruh proses budidaya sayuran hidroponik dengan menggunakan bibit yang siap tanam. Knowledge map digunakan untuk kodifikasi pengetahuan menghasilkan 34 pengetahuan explicit berupa media interaktif video maupun dokumen yang dapat digunakan oleh pengguna. Implementasi sistem menggunakan aplikasi berbasis Web dengan pendekatan object oriented yang sudah diuji oleh pakar dan semua fungsi berjalan dengan baik. Sistem ini memiliki fitur klasifikasi KMS yaitu knowledge capture, knowledge sharing, serta knowledge discovery. AbstractThe increasing number of populations in Indonesia has an impact on the limited area in Bogor, which has resulted in the threat of agricultural production because of the conversion of 88.12% of paddy fields to housing and gardens. A solution to agricultural production systems with limited land, one of which is hydroponics. Facilitate access to digital learning facilities as an essential part of the plan of the Bogor City government, namely smart society, information technology support is needed as a means of sharing hydroponic cultivation knowledge. Based on this, we developed a knowledge management system (KMS) adopting the Knowledge Management Life Cycle method by identifying tacit and explicit knowledge from the hydroponic community. The process of capturing knowledge is oriented to the whole process of hydroponic vegetable cultivation by using seeds that are ready for planting. Knowledge map is used for codification of knowledge that produces 34 explicit knowledge in the form of interactive media in the form of videos and documents that can be used by user. The output generated from this study is KMS was implemented using Web-based applications with an object-oriented approach that has been tested by experts with system functions is working and has KMS classification features, namely knowledge capture, knowledge sharing, and knowledge discovery.
Optimasi DF Berbasis Posisi Jendela Menggunakan Estetika Asimetris
Authors: Rizal Yose , Robandi Imam , Yuniarno Eko Mulyanto 
Published: June 2020

Jendela merupakan elemen arsitektur yang berfungsi memasukan pencahayaan alami dari luar kedalam bangunan. Distribusi pencahayaan siang yang merata masuk kedalam ruangan dapat meminimalkan penggunaan pencahayaan buatan. Penelitian ini dilakukan untuk mencari optimasi posisi jendela dengan pendekatan kepada distribusi jarak daylight factor (DF) dan estetika komposisi spasial. Analisis dilakukkan berdasarkan kualitatif distibusi DF pada ruangan terhadap posisi bukaan jendela secara estetika komposisional beradasarkan grafik teori kecocokan represetasional. Tahap fitting dan normalisasi pada nilai daylight factor dan estetika asimetri dari teori kecocokan represetasional merupakan langkah awal untuk mendapatakan optimasi nilai faktor yang nantinya akan diberikan suatu nilai faktor bobot ( ) untuk kedua variabel. Hasil studi di dapat posisi jendela paling optimal berada pada posisi jarak 23% dari lebar bidang dinding bukaan jendela terhadap sisi jendela bagian dalam, dimana nilai faktor bobot = 0,5. Optimasi dengan menggunakan nilai dapat digunakan arsitek untuk menentukkan distribusi DF, estetika asimetri atau bahkan keduanya. AbstractThe window is an architectural element that functions to incorporate natural lighting from outside into the building. Even daylight distribution into the room can minimize the use of artificial lighting. This research was conducted to find window position optimization by approaching the daylight factor (DF) distance distribution and spatial composition aesthetics. The analysis was carried out based on the qualitative distribution of DF in the room to the position of the window opening aesthetically compositional based on a graph of the theory of repetational compatibility. The fitting and normalization phase of DF values and the asymmetry aesthetics of the repetational match theory are the first step to get an optimization of the factor values which will be given a weight factor value (α) for both variables. The results of the study in the most optimal window position can be located at a distance of 23% from the width of the window opening wall area to the inner side of the window, where the weight factor value α = 0.5. Optimization using the can be used by architects to determine the distribution of DF, asymmetry aesthetics or even both.
Psychological nature of intelligence officer
Authors: Naumov P. Yu. , Povshednaya F. V. 
Published: June 2020

Introduction. Based on modern social trends, the demand becomes not only for professionally trained people, but also for the level of their general culture, value system and, ultimately, intelligence. At the same time, there is no place for intellectuals and educating intellectuals in program documents on educational activities, although this task is very logical for the pedagogical practice of a developed society. This work presents the experience of the author's analysis of the psychological nature of the intelligence of an officer. Consistently considering the essence and structure of such a complex phenomenon, the structure and the real functioning of the values that allow characterizing the subject as an intellectual are ascertained.Materials and methods. As the main research methodology, the authors use sociological (I.S. Kon), culturological adapted to solve the problems of this work (M.S. Kagan), historiographic (A.V. Popov), systemic (I.V. Blauberg, V.A. Lektersky, V.N. Sadovsky, S.L. Rubinstein, M.S. Kagan, N.V. Kuzmina) and functional approaches (P.K. Anokhin, M.S. Kagan, N. Wiener). The main research methods were: hypothetical-deductive method; analysis, synthesis, comparison, analogy and abstraction; systemic method and modeling.Results. The result of the study is that the authors identified and justified the structural psychological qualities of intelligence as the subjective characteristics of an officer and examined the basic mechanisms of formation of intellectual values.Discussion and Conclusions. The required criteria for being intelligent as a subject characteristics of an officer is the level of education (self education)of an officer, his manners, the scope of his values , existential assessment –correlating every fact he faces with general life-span problems of objective reality, having respect for values of others and being ready for talk to employees and superiors as well as the representatives of other social groups, other cultures, nationalities, confessions and professions which requires dialog in search of optimal forms and options of interaction. The cornerstone principle for intelligence of the officer are, therefore, his education and upbringing, ideological conviction in his own values and readiness for self-sacrifice for their sake.
Electronic education system as an instrument of increasing the specialist's competitiveness in digital economy
Authors: Malushko E. Yu. , Lizunkov V. G. 
Published: June 2020

Introduction. The article describes the e-education system, modern e-learning tools and their role in enhancing the competitiveness of a graduate of a modern educational institution, in particular, concerns applying graduate’s competences in modern conditions of the territories of advanced social and economic development.Materials and methods. The author considers the competency-based characteristics of the involved teacher, diverse electronic educational resources and their types, their advantages and certain disadvantages, as well as their prospects in the perspective of the educational subject.Results. Great importance is given to the motivation of the specialist, including educational and cognitive aspects. Potential competitiveness directly depends not only on the educational and cognitive motivation that drives the learning process and allows the specialist to acquire the maximum amount of specialty-related knowledge and skills, but also on the professional one, which provides the professional orientation.Discussion and Conclusions. The social significance and the prospect of implementing electronic education system in Russian reality, especially in the context of economic, geographical and social inequality at the territories of advanced social and economic development, are revealed. Elements of e-education, adequately integrated into the vocational training system, are able to provide increased competitiveness and adaptability of a specialist in the digital economy.
EMOTIONAL INTELLIGENCE AND SELF-DISCIPLINE IN SENIOR HIGH SCHOOL
Authors: Moneva Jerald C. , Gatan Bandino P. 
Published: June 2020

This study examined the relationship between emotional intelligence and self- discipline in schools. Emotional Intelligence and Self-discipline are important factors in influencing the students to achieve academically. Emotional Intelligence is the ability to understand, use and manage their own emotion while self-discipline is a practice, a habit, a skills to understand the important factors in life of every individuals. This study is a descriptive correlation that described the two variables, if they had a connection. However, the results were determined if the two variables have a relationship that will affect the students discipline and their emotional intelligence. A students’ questionnaire was constructed to obtain data on the students’ emotional intelligence and self- discipline. Chi- square was used to establish whether differences in emotional intelligence between self-discipline and it will serve as the basis to identify the level 0f both emotional and self-discipline. The purpose of sampling was done targeting to all the senior high school students. With a target of 245 students who are qualified to answer the questionnaire that I was provided. Emotional Intelligence and Self-discipline is a way that make the students to be aware about the factors in achieving their goals that make the students to be an independent individual. It makes as the basis to manage their own emotions. The data collected using questionnaire was cleaned, encoded, analyze and then interpreted for processing of result. The student’s level were compared and analysis made on which discussion was based. The average weighted mean will be used to determine the assessment of the respondents with regards to their personal profile.