bluuebunny/crossref_metadata_2025 huggingface dataset:
Pengaruh Penerapan E-Billing dan E-Filling Terhadap Kepatuhan Wajib Pajak Dengan Pemahaman Perpajakan Sebagai Variabel Moderating
Authors: Hanum Zulia , Mulyawan Muhammad Ikhwan
Published: March 2024

The general aim of this research is to understand the impact of implementing E-Billing and E-Filing systems on taxpayer compliance, with taxation understanding as a moderating variable, among individual taxpayers. The study involves a sample of 217.725 individuals, with 100 taxpayers selected as participants from the tax object population. The research methodology employed is an Associative approach, focusing on exploring relationships or influences among variables. Data collection is conducted through questionnaires, consisting of written questions presented to respondents to measure their attitudes, opinions, and perceptions regarding social phenomena. Data analysis is including tests for data quality, classical assumption tests, multiple linear regression, and model feasibility tests involving 100 individual taxpayers. The results of this study indicate that the E-Billing system does not have a significant impact on taxpayer compliance, with a significance value of 0.002 < 0.05 and a t-value of 1.159 < 1.984. Conversely, the E-Filing system significantly influences taxpayer compliance, with a significance value of 0.000 < 0.05 and a t-value of 5.316 > 1.984. Collectively, the implementation of the E-Billing and E-Filing systems has a significant impact on taxpayer compliance, with a significance value of 0.000 < 0.05 and an F-value of 20.714 > 3.09. Respondents' perceptions regarding the taxation understanding variable strengthen the relationship between the implementation of the E-Billing and E-Filing systems and taxpayer compliance.
Pengaruh Rasio Keuangan Terhadap Harga Saham
Authors: Primasatya Rieswandha Dio , Arliana Agatha
Published: March 2024

This research aims to examine the impact of financial ratios such as Fixed Asset Turn Over, Return On Asset, Current Ratio, Total Asset Turnover, and Debt To Equity Ratio on stock prices of manufacturing companies in the consumer goods sector in Indonesia. The sample was selected from companies listed on the IDX for five consecutive years during the period 2015-2020, with consistent annual financial reports, and listed in the LQ45 during the 2020 period. The research findings indicate that Fixed Asset Turn Over has a significant negative relationship with stock prices, Return On Asset has a significant positive relationship with stock prices, Current Ratio shows a negative but not significant relationship with stock prices, Total Asset Turnover has a positive but not significant relationship with stock prices, and Debt To Equity Ratio has a negative and not significant relationship with stock prices. Subsequent research may consider other factors that influence stock prices and assess their impact on the variables under investigation
Evaluasi Implementasi Pengawasan Berbasis Kewilayahan: Studi Kasus KPP Pratama X
Authors: Satyanugraha Muhammad Ridho , Abbas Yulianti
Published: March 2024

Regional-based supervision is a new policy implemented by the Directorate General of Taxes in 2020 and revised in 2022. This study aims to evaluate factors related to the implementation of regional-based supervision, which are top management support, regulations, regional account representatives, taxpayers, and information systems. The factors are further divided into thirteen indicators. Previous studies on regional-based supervision and/or tax supervision in general mostly focused on the influence of tax supervision activities on taxpayer compliance or tax revenues. This study differs from previous study because it seeks an in-depth understanding of the implementation of supporting factors in regional-based supervision via an in-depth case study. The interviews involved the office head, the supervision section head, regional account representative, tax consultant, and taxpayers. The results of the study indicate a weakness in the implementation of regional-based supervision related to regulation, both in terms of the availability of regulation that regulate interactions among the parties involved and the clarity in the field of taxation. Additionally, the available budget and the number of account representative personnel still need to be improved.
Evaluasi Penerapan Batasan Omset Tertentu Tidak Dikenakan PPh Final (Studi Kasus KPP Pasar Rebo)
Authors: Sibarani Yolla Angela Rulianty , Rosid Arifin
Published: March 2024

UMKM memiliki peran yang besar bagi perekonomian Indonesia, namun fakta tersebut tidak sesuai dengan kontribusi pajaknya. Penerimaan pajak yang rendah dari sektor UMKM tersebut menggambarkan kepatuhan perpajakan yang juga rendah. Penelitian ini bertujuan untuk melakukan evaluasi terhadap kebijakan batasan sebesar Rp 500 Juta tidak dikenakan Pajak Penghasilan Final untuk WP OP UMKM yang diatur dalam PP Nomor 55 Tahun 2022. Evaluasi dilakukan menggunakan kriteria yang diterbitkan oleh OECD (2021) yaitu relevansi, efektivitas, dampak dan koherensi. Penelitian ini menggunakan metode studi kasus jenis evaluasi dengan pendekatan kualitatif deskriptif. Objek studi kasus adalah KPP Pratama Jakarta Pasar Rebo yang memiliki jumlah Wajib Pajak UMKM terbanyak di wilayah Jakarta Timur. Pada penelitian ini digunakan teknik pengumpulan data berupa data sekunder, studi pustaka dan data primer. Data sekunder yang digunakan yaitu tingkat kepatuhan Wajib Pajak UMKM KPP Pratama Pasar Rebo dalam 5 (lima) tahun terakhir yang diperoleh dari Direktorat Jenderal Pajak. Data primer adalah hasil wawancara mendalam kepada beberapa pihak yang berhubungan langsung dengan penerapan kebijakan tersebut, yaitu Wajib Pajak dan pemerintah. Dasar pemilihan narasumber adalah purposive sampling dan wawancara dilakukan dengan cara semi-terstruktur. Hasil penelitian menunjukkan bahwa kebijakan batasan Rp 500 Juta tepat untuk kondisi UMKM saat ini karena memberikan kemudahan dan keadilan bagi Wajib Pajak Orang Pribadi UMKM. Selain itu kebijakan tersebut menghasilkan dampak berupa peningkatan kepatuhan perpajakan. Kebijakan batasan Rp 500 Juta selaras dengan kebijakan perpajakan lainnya.
Penentu Faktor Fundamental dan Belanja Modal Terhadap Harga Saham Perusahaan Fast Moving Consumer Goods yang Terdaftar di Bursa Efek Indonesia Tahun 2017-2021
Authors: Putra Rizky Dwi
Published: March 2024

Each company is competing to advance its business in order to maximize profit from the business activities it engages in and to confront yearly competition. This study aims to investigate fundamental factors such as earning per share, return on asset, return on equity, book value per share, and capital expenditure on the stock prices of fast-moving consumer goods companies listed on the Indonesian Stock Exchange from 2017 to 2021. 99 samples of corporations listed on the Indonesian Stock Exchange are involved in the fast-moving consumer goods industry. The method of data analysis utilized in this investigation is multiple regression analysis. The results of this study is arning per share, return on asset, return on equity, book value per share, and capital expenditure have a significant impact on the stock prices simultaneously. Partially Earning per share, return on asset, return on equity, book value per share has a significant and positive effect on stockprice while capital expenditure has an insignificant effect. Implication of this study is company must always maintain and improve its financial and operational performance to maintain positive investor sentiment and capital market signals. Future research can employ objects from company listed on the Indonesian stock exchange with positive capital expenditure performance
Faktor Determinan Kualitas Laba dengan Profitabilitas sebagai Variabel Moderasi
Authors: Anjani Nadya Zalianti Puspa , Wahyuni Sri , Setyadi Edi Joko , Mudjiyanti Rina
Published: March 2024

This study aims to measure the effect of liquidity, company size and accounting conservatism on earnings quality with profitability as a moderating variable. This research data was collected through secondary data sources obtained through the website www.idx.co.id or the official website. The population taken was 77 companies operating in the food and beverage sector. During the 4 year observation period, namely 2018 to 2021, the total sample used was 120 companies in this sector. In analyzing the data, this research used descriptive statistical test techniques, classical assumption tests, multiple linear regression (MRA), and hypothesis testing using the SPSS application. The results of hypothesis testing prove that liquidity has a significant positive effect on earnings quality, while company size, accounting conservatism, and profitability do not have a significant effect on earnings quality. In the moderation test, profitability cannot moderate the effect of liquidity, company size and accounting conservatism on earnings quality.
Koneksi Politik dan Pengungkapan Corporate Social Responsibility: Tinjauan Literatur dari Berbagai Negara
Authors: Mardiana Fitra , Suprayoga Suprayoga , Pratiwi Lily Indah
Published: March 2024

This research explains the relationship between political connections and CSR disclosure using a library research approach. Through the exploration and integration of findings from previous empirical research, this study aims to present a more comprehensive understanding of the relationship between political connections and CSR disclosure in various countries. The results of the study indicate that the connection between political connections and CSR disclosure is influenced by the institutional factors of the country where the company operates. Furthermore, the research also shows that political connections provide benefits to companies in developing, advanced, and transforming economies, but these benefits are more significant in developing countries. This study contributes to expanding the literature on political connections and CSR disclosure by presenting empirical results from various countries.
Literature Review : Strategi Sistem Single Identity Number (SIN) dalam Optimalisasi Penerimaan Pajak
Authors: Uswatun Hasanah
Published: March 2024

Adanya ketidakseimbangan antara potensi penerimaan pajak dan realisasinya akan selalu menjadi perhatian yang mendalam, hingga harus dicari solusinya. Salah satu solusi yang sedang dilakukan oleh pemerintah saat ini adalah penggabungan NIK menjadi NPWP. Penelitian ini dilakukan untuk menganalisis potensi Single Indentity Number (SIN) sebagai salah satu perluasan basis pajak dalam meningkatkan efisiensi penerimaan pajak. Menggunakan pendekatan Literatur Review, artikel ini berupaya mengidentifikasi potensi sistem Single Indentity Number (SIN) dalam meningkatkan penerimaan pajak. Penelitian ini merupakan sintesis dari penelitian terdahulu yang akan mendeteksi potensi besar perluasan basis pajak dalam meningkatkan penerimaan pajak melalui sistem Single Indentity Number (SIN). Penelitian ini diharapkan akan mendukung keberlanjutan fiskal, efisiensi penggunaan sumberdaya, maksimalisasi penerimaan pajak untuk pembangunan yang secara tidak langsung berkontribusi pada pembangunan berkelanjutan. Hasil penelitian menunjukkan dengan adanya SIN maka pemerintah akan mudah mengakses informasi yang relevan dalam pengenaan pajak; SIN dapat digunakan untuk memonitor kepatuhan wajib pajak, mengidentifikasi kecurangan, serta pengawasan pajak; SIN juga akan mendorong peningkatan penerimaan pajak sejalan dengan bertambahnya basis data pajak yang lebih akurat dan berkesinambungan. Sehingga dapat disimpulkan bahwa dengan dilaksanakan sistem Single Indentity Number (SIN) maka akan memutakhirkan basis data pajak dan memaksimalkan penerimaan pajak dengan catatan pengamanan data dan privasi wajib pajak harus lebih diperketat.
Evaluasi Sistem Pengendalian Internal atas Pertanggungjawaban Bantuan Pemerintah pada Badan X
Authors: Hendryani Sary , Siswantoro Dodik
Published: March 2024

Sistem pengendalian internal berperan dalam meningkatkan kinerja, transparansi, dan akuntabilitas pengelolaan keuangan negara. Salah satu tujuan penyaluran bantuan pemerintah yaitu untuk memperluas akses dan meningkatkan kualitas pendidikan di Indonesia. Untuk itu pengelolaan bantuan pemerintah harus dilaksanakan dengan efektif, efisien, transparan, dan akuntabel, dalam rangka mendukung perluasan akses dan peningkatan kualitas pendidikan di Indonesia. Adanya temuan Badan Pemeriksa Keuangan terkait pertanggungjawaban bantuan pemerintah, mengindikasikan terdapat permasalahan pada sistem pengendalian internal. Penelitian ini bertujuan untuk mengevaluasi penyebab kurang optimalnya penerapan sistem pengendalian internal dalam pertanggungjawaban bantuan pemerintah, dan diharapkan penelitian ini dapat menghasilkan implikasi dalam rangka meningkatkan sistem pengendalian internal. Objek penelitian ini yaitu Badan X yang merupakan badan independen pemerintah yang bertugas melakukan penilaian satuan pendidikan di tiga puluh empat provinsi. Penelitian ini dilakukan dengan metode kualitatif, menggunakan pendekatan studi kasus. Data yang digunakan terdiri dari data primer dan data sekunder. Data primer diperoleh dari wawancara semi struktur, terhadap informan yang memiliki latar belakang dan pengalaman terkait pertanggungjawaban bantuan pemerintah pada Badan X. Data sekunder diperoleh dari analisis dokumen. Hasil penelitian menunjukkan bahwa terdapat risiko yang terjadi karena penetapan kebijakan yang tidak sesuai peraturan mendasarnya dan risiko akibat penetapan peraturan baru. Rekomendasi yang diberikan yaitu mengefektifkan dan melibatkan peran Satuan Pengawasan Internal dalam pengawasan kebijakan.
SROI Program Lingkungan Peternakan Sapi Terintegrasi (Literasi) CSR Petrokimia Gresik : Pendekatan Present Value dan Future Value
Authors: Virnandasari Ayu , Mawardi Fahmi Dwi
Published: March 2024

Corporate Social Responsibility (CSR) erat kaitannya dengan pembangunan berkelanjutan, Perusahaan dalam menjalankan proses bisnis tidak hanya memperhatikan aspek keuangan saja, tetapi juga harus memperhatikan aspek sosial dan lingkungan. PT Petrokimia Gresik telah berkomitmen untuk ikut serta dalam program CSR dengan membuat program Lingkungan Peternakan Sapi Terintegrasi (Literasi) dengan tujuan mengajak peternak untuk mengelola limbah peternakan dan pertanian agar dapat meningkatkan nilai jual. Tujuan dari penelitian ini yaitu untuk menganalisis hasil perhitungan dampak Social Return on Invesment (SROI) bagi kelompok Literasi yang berlokasi di Desa Sumbersari, Kec. Sambeng, Kab. Lamongan. Hasil analisis SROI PT Petrokimia Gresik berdasarkan perhitungan present value menghasilkan rasio 3,44 sedangkan perhitungan future value menghasilkan rasio 3,87. Sehingga dengan hasil rasio future value semakin tinggi, maka program Literasi layak dijalankan di tahun berikutnya dan berhasil memberikan manfaat secara ekonomi, sosial, dan lingkungan.
Pengaruh Intellectual Capital terhadap Kinerja Keuangan pada Bank Syariah di Malaysia Tahun 2019-2022
Authors: Shara Yuni , Kholis Azizul , Ikhsan Arfan
Published: March 2024

This study aims to investigate the effect of Intellectual Capital on the financial performance of Islamic banks in Malaysia during the period 2019-2022. This research method uses a quantitative approach by collecting data from the annual financial statements of Islamic banks in Malaysia during the study period. Intellectual Capital is measured by Capital Employed Efficiency, Human Capital Efficiency and Structural Capital Efficiency. Financial performance is measured by ROA (Return on Assets). The data analysis technique uses Partial Least Square (PLS). The sample was selected based on purposive sampling and there were 13 Islamic Banks for 4 years. The results showed that intellectual capital has a positive and significant effect on financial performance. This finding indicates that the management and development of intellectual capital, such as efficient use of capital, improving employee skills, and strengthening organizational infrastructure, contribute positively to the financial performance of Islamic banks in Malaysia. The practical implication of this study is that the management of Islamic banks should pay special attention to the management of their intellectual capital as part of the strategy to improve ROA. Investment in intellectual capital development can generate long-term benefits for Islamic banks, especially in the face of intensifying competition in the banking industry.
Penghindaran Pajak dan Nilai Perusahaan: Modal Intelektual Sebagai Pemoderasi
Authors: Khaled Januar , Abbas Yulianti
Published: March 2024

Each company will evaluate the performance that has been carried out using company value as an indicator. Company value is the real value that comes from the investor's perspective which relates the company to the share price. The aim of this research is to analyze the relationship between tax avoidance and company value, and the moderating influence of intellectual capital on the relationship between tax avoidance and company value. The data used in this research are all companies listed on the IDX in 2018-2022. The research sample after purposive sampling was carried out was 175 companies. This research uses unbalanced panel data, so the total data sample is 627. The research results show that tax avoidance is negatively related to company value and intellectual capital cannot moderate the relationship between tax avoidance and company value. The conclusion of this research is to prove that tax avoidance has a negative effect on company value, so that if a company engages in tax avoidance behavior it will reduce the value of the company related to agency problems, and this research explains that when intellectual capital is used as a moderator it shows results that are not related to company value. , this explains that the company's intellectual capital has no effect when there is a agency problem.
Implementing Integrated Reporting to Disclose Intellectual Capital in University
Authors: Rachmawati Rima , Sari Diana , F. Rodelas Allan Nicko
Published: March 2024

The research aim is to provide empirical and theoretical solutions for certain criticalities in the framework of Integrated Implementation Reporting (IIRF) within the context of higher education. Indeed, IIRF considers value creation only from the perspective of capital enhancement spurred by academic activities, overlooking the fulfillment of institutional missions as the true lever of value creation. This paper introduces a case study aimed at implementing IIRF at a University in Indonesia. The research is based on theory building from the case, action research, and intervention approaches. IIRF was chosen for its claimed ability to support communication processes with stakeholders and control over value creation, despite demonstrating some weaknesses. It highlights the role played by intellectual capital in the business model of organizations and in the value creation process. This adjustment appears to increase awareness of the role of intellectual capital in value creation at universities.  In this paper, not one part of the adapted framework is innovative on its own, but collectively they provide an innovative solution, addressing the disclosure and managerial needs of the studied organization. The single case study allows us to test the claimed weaknesses of IIRF in the literature, suggest some adjustments to the original framework, and validate its effectiveness. Thanks to the single case study, we were then able to construct a theoretical framework that develops theory inductively; now the suggested framework can be tested and further validated in other organizations. This paper introduces an innovative approach to reporting and disclosing intellectual capital in universities. It is relevant not only for external communication but also for internal purposes, aiding managers in decision-making and actions.
Music Genre Classification Using K-Nearest Neighbor and Mel-Frequency Cepstral Coefficients
Authors: Pratiwi Tika , Sunyoto Andi , Ariatmanto Dhani
Published: March 2024

Music genre classification plays a pivotal role in organizing and accessing vast music collections, enhancing user experiences, and enabling efficient music recommendation systems. This study focuses on employing the K-Nearest Neighbors (KNN) algorithm in conjunction with Mel-Frequency Cepstral Coefficients (MFCCs) for accurate music genre classification. MFCCs extract essential spectral features from audio signals, which serve as robust representations of music characteristics. The proposed approach achieves a commendable classification accuracy of 80%, showcasing the effectiveness of KNN-MFCC fusion. Nevertheless, the challenge of overlapping genres, particularly rock and country, demands special attention due to their shared acoustic attributes. The inherent similarities between these genres often lead to misclassification, hampering accuracy. To address this issue, an enhanced feature engineering strategy is devised, leveraging deeper insights into the subtle nuances that differentiate rock and country music. Additionally, a refined KNN distance metric and neighbor selection mechanism are introduced to further refine classification decisions. Experimental results underscore the effectiveness of the refined approach in mitigating genre overlap issues, significantly enhancing classification accuracy for rock and country genres. This study contributes to the advancement of music genre classification techniques, offering an innovative solution for handling overlapping genres and demonstrating the potential of KNN-MFCC synergy in achieving accurate and refined genre classification.
Integrated Selection of Permanent Teacher Appointments Recommended MCDM-AHP and WASPAS Methods
Authors: Akmaludin Akmaludin , Sihombing Erene Gernaria , Rinawati Rinawati , Dewi Linda Sari , Arisawati Ester
Published: March 2024

The teacher's role is very important in improving the national learning system. Many honorary teachers are empowered in curriculum development in a number of schools who want to collaborate in improving the quality of their students. The purpose of this research is to provide rewards to honorary teachers who have long served for the progress of the nation in the world of education to be appointed as permanent teachers. The selection method was carried out through a criteria weighting technique with the MCDM-AHP method which was integrated with the WASPAS method. The technique of developing the MCDM-AHP method as an eigenvector measurement concept with proof of optimization through mathematical algebra matrices that is correlated with the Expert Choice application to get optimal values. The result optimization value is integrated with the WASPAS method as a determinant of the ranking system for permanent teacher candidates. This method is a unification of the concepts of the weight product model and weight sum model methods, so that it has special stages to support decision making with the WASPAS method. The results of selecting twelve honorary teachers for appointment as permanent teachers can be seen from the acquisition of the Qi optimization value as a ranking. The results of support for decision making for permanent teacher appointments with the highest optimization value were given to TC04 with a weight of 0.878; followed by a significant difference in the next rank. The findings of this study provide evidence that the integration of the MCDM-AHP and WASPAS methods provides continuous optimization results for decision-making support.
Comparison of Smartphone Technology using AHP, ELECTRE, and PROMETHEE Methods
Authors: Akmaludin Akmaludin , Suriyanto Adhi Dharma , Iriadi Nandang , Widianto Kudiantoro
Published: March 2024

The progress of smartphone technology is now very rapid, supported by many renewable features, even many users are competing to get the latest products without regard to the costs that have been incurred. The problem that arises is that it is increasingly difficult to select technology-based products with many criteria. The purpose of writing this paper is to provide the best solution for selecting technology-based products with multi-criteria to suit user needs by taking into account the costs incurred effectively and the use of contradictory multi-criteria applications. The presence of technology products always has many criteria that make it more difficult for users to choose products as the right choice according to their needs, thus the right method is needed as a solution to obtain technology-based products such as smartphones. The Analytic Hierarchy Process (AHP) method is used for the evaluation and selection process. This AHP method will collaborate with the ELECTRE and PROMETHEE methods as a comparison solution for smartphone product selection. The resulting comparison will be an applied model for smartphone selection that produces the best decision-making support according to user needs. The results of the collaborative implementation process of the ELECTRE and PROMETHEE methods provide a decision on the rating system. The collaborative application of the AHP method to the ELECTRE and PROMETHEE methods provides optimal decision support for the selection process, so that this can be used as a comparison material in making decisions regarding the selection of smartphones as technology-based products.
Analyzing Public Sentiment Regarding the Qatar 2023 World Cup Debate Using TF-IDF and K-Nearest Neighbor Weighting
Authors: Olajuwon Sayyid Muh. Raziq , Kusrini Kusrini , Kusnawi Kusnawi
Published: March 2024

This research aims to uncover the sentiment of Twitter users regarding the polemics surrounding the 2023 Qatar World Cup using a text-based sentiment analysis approach. The research methodology involves collecting data from Twitter posts, encompassing discussions, opinions, and responses related to the Qatar World Cup 2023. The TF-IDF weighting is applied to identify significant keywords in each post, while the K-Nearest Neighbor algorithm is employed to classify sentiments as positive, negative, or neutral. The findings reveal a comprehensive picture of how the public perceives the Qatar World Cup 2023 on the Twitter platform. The results not only cover positive and negative aspects of online discussions but also identify trends and patterns of sentiment that emerge during specific periods.The application of these methods provides valuable insights into understanding the dynamics of public opinion related to international sports events through the lens of social media. The results of the analysis demonstrate that a majority of Twitter users express positive sentiments towards the Qatar World Cup 2023, highlighting excitement and anticipation. However, some negative sentiments also arise, primarily related to controversies and concerns about the event. The research further identifies temporal variations in sentiment, reflecting changing public perceptions over time.This research contributes to the development of sentiment analysis methods by using a combination of TF-IDF weighting and the K-Nearest Neighbor algorithm to delve into Twitter users' perspectives. Consequently, the findings have practical applicability for further research and implementation in managing the social impact and public perception of major sporting events like the World Cup. .
Comparison of CNN and SVM Methods on Web-based Skin Disease Classification Process
Authors: Kushartanto Ahmad Ilham , Fauziah Fauziah , Aldisa Rima Tamara
Published: March 2024

Skin, as the outermost layer of the body, is often in contact with bacteria, germs and viruses because of its most external position. According to statistics from the 2009 Indonesian Health Profile, skin illness is the third most common ailment seen in outpatient settings across the country's hospitals. Therefore, maintaining healthy skin is important because it protects the body's internal organs from injury and attack by pathogens. The development of image classification, such as the classification of skin diseases, has become a focus in the health sector. This research analyses the performance of Convolutional Neural Network (CNN) and Support Vector Machine (SVM) in web-based skin disease classification and overcomes the problem of imbalanced training data. With data augmentation and preprocess, this research improves data generalization and compares performance metrics such as Recall, Accuracy, and F1 Score. The results show that the average accuracy of CNN is 83.8%, while SVM reaches 81%. Although both models have high metrics for the normal class, other more complicated classes can only be handled by CNN with a value of more than 0.9. Apart from that, the CNN method also provides a higher Confidence Score than SVM, as well as a faster execution time. In conclusion, the CNN method is superior and recommended for skin disease classification based on web applications based on various performance test results.
Effect of Epoch Value on the Performance of the RNN-LSTM Algorithm in Classifying Lazada App Review Sentiments
Authors: Putra Maswan Pratama , Sibaroni Yuliant
Published: March 2024

In today's development, the process of buying and selling transactions between sellers and buyers is so developed. not only done directly but can also be done online or can be called e-commerce. Which is where the development of technology is so fast that it indirectly encourages entrepreneurs to develop through e-commerce. Lazada is one of the online stores in Indonesia that has many users and Lazada makes it easy to shop without the need to come to the place or directly. However, purchasing goods using e-commerce has problems regarding the quality of the goods you want to buy, therefore purchasing goods can be seen through reviews of each one you want to buy. Sentiment analysis is carried out using the Recurrent Neural Network (RNN) method with Long Short Term Memory (LSTM). And using the Epoch value as a parameter in processing validation data and test data to produce the best accuracy value
Development of an Intelligent Imaging System for Determining Maturity of Copra Flesh in Coconuts Using Shape and Texture Extraction
Authors: Wiyandra Yogi , Yenila Firna , Wahyuni Suci
Published: March 2024

Copra is dried coconut meat that is used to produce coconut oil. According to the Central Statistics Agency (BPS), Indonesia's copra production in 2020 reached 2.3 million tonnes. This is one form of the process of improving the economy of people living on the coast. This research was conducted to educate farmers in determining the level of maturity of the copra meat produced. This research was conducted using an extraction method that involves colour extraction and texture extraction. the method is used to provide convenience in seeing the level of maturity of the two characteristics of copra obtained in the field, namely texture and colour. The process obtained in the training with one of the images used as a test image in colour extraction produces area, perimeter, metric and eccentricity values in label 3 with values of 651.00, 184.69, 0.24 and 0.89. while in the feature extraction method the results are obtained with an average intensity value of 243.31, standard deviation of intensity 39.76 and entropy value of the tested image 4.57. The method is able to perform a detection process so that it can determine the level of maturity of copra seen from the existing types of copra such as asalan copra, regular copra, black copra and wet copra, each of which provides different functions in the copra processing stage. The process will be carried out using KNN which is seen from all test data and training data stored after the detection process. The results of the process carried out using digital images involving the extraction method for detection and KNN for classification are able to provide the right value. This is evidenced by the better accuracy value of 98%.
Analysis of TF-IDF and TF-RF Feature Extraction on Product Review Sentiment
Authors: Harmandini Keisha Priya , L Kemas Muslim
Published: March 2024

Sentiment analysis of product reviews is critical in understanding customer views and satisfaction, especially in the context of e-commerce applications. A marketplace provides channels where users can submit reviews of the products they purchase. However, due to the large number of reviews in a marketplace, analyzing them is no longer feasible to be performed manually. This research proposes a machine learning implementation to perform sentiment analysis on product reviews. In this research, the product review dataset on Shopee marketplace is used for sentiment analysis by comparing TF-IDF and TF-RF feature extraction using the SVM algorithm with stages of dataset, labeling, feature extraction and accuracy results. The importance of the comparison between TF-IDF and TF-RF feature extraction in this research is related to the need to evaluate and determine which feature extraction method is most effective in increasing the accuracy of sentiment analysis. TF-IDF and TF-RF are two methods commonly used in text analysis, and a comparison of their performance can provide deep insight into the effectiveness of each in the context of product sentiment analysis.Thus, through this comparison, this research aims to determine the best approach that can provide the highest accuracy results, so that the results can serve as a guide for further research. Based on the evaluation, the highest accuracy value is achieved at 92.87% by using TF-IDF and SVM classifiers which outperformed previous research.
Designing an Application for Detecting Diseases of Rice Plants Using OOAD Method
Authors: Khalil Wijdan , Irsan Muhammad , Fathoni Muhammad Faris
Published: March 2024

Rice, as a key element of Indonesia's food security, plays a crucial role in agricultural ecosystems. Despite its high economic value, rice plants are susceptible to various diseases that can reduce productivity and harvest quality. Farmer's limited knowledge about disease types, identification, and proper handling poses a serious challenge to sustainable agriculture. Previous studies highlight farmers' inadequate understanding of pests and diseases in rice plants, leading to a high dependency on pesticides. Furthermore, lack of training data and a shallow understanding of rice diseases present significant challenges in disease management efforts. This research aims to develop an Android-based Smart Farm application. This application utilizes image processing and artificial intelligence technologies to assist farmers in identifying leaf diseases in rice plants. Requirements analysis involves literature review and field observations around Bandung Regency. It can be concluded; Smart Farm application has been successfully developed with three functional and two non-functional requirements. Validation testing indicates a 100% functionality rate and an 80% accuracy in disease detection. Nevertheless, further attention is required to enhance accuracy by providing more training data and improving image quality. The implications of this research extend to enhancing farmers' knowledge, reducing pesticide dependency, and supporting sustainable agriculture in the future.
Sentiment Analysis of the 2024 Indonesia Presidential Election on Twitter
Authors: Damayanti Lisyana , Lhaksmana Kemas Muslim
Published: March 2024

This analysis enables the identification and a deeper understanding of the positive and negative sentiments reflected in online conversations, providing a comprehensive view of the direction of public support and preferences regarding presidential candidates. Sentiment analysis through machine learning can manage extensive sentiment data, ensuring time efficiency, and enhancing accuracy in swiftly and comprehensively comprehending people's opinions and preferences. With these advantages, machine learning-based sentiment analysis has gained popularity as an effective choice for understanding people's perspectives, preferences, and responses to various issues and events. Therefore, this research focuses on sentiment analysis regarding public opinions on the 2024 presidential election. The method employed in this research is the SVM algorithm with Word2Vec feature extraction. The researcher is interested in conducting a study related to sentiment analysis of the 2024 Indonesian Presidential election using the Support Vector Machine algorithm because of its high accuracy compared to other algorithms. The use of feature extraction aims to improve the performance and effectiveness of the algorithm, and Word2Vec is chosen because it can represent contextual similarity between two words in the generated vectors, enabling concise and improved text classification based on context. The results of this research indicate the best performance at 80:20 ratio with a precision score of 88,94%, Recall 93.08%, F1-score 90,43% and accuracy of 90,75%. This study's results outperform prior research using the SVM method, which achieved an 82,3% accuracy.
Rice Plant Disease Detection System Using Transfer Learning with MobilenetV3Large
Authors: Faqih Rifqi Raenanda , Irsan Muhamad , Fathoni Muhammad Faris
Published: March 2024

In this study, we address that foliar diseases of rice (Oryza sativa L.) pose a serious threat to agricultural productivity and propose an effective method for disease detection using Convolutional Neural Network (CNN). We use transfer learning on the MobilenetV3Large model to improve the model's performance. Our study involves a curated dataset containing images of infected rice leaves, followed by a careful preprocessing step. This dataset is then used to train a CNN model. The results show a commendable accuracy rate of over 90% and almost reaching 95% when the model is trained over 200 epochs. The model performance graph shows a consistent upward trend in accuracy coupled with decreasing loss during the training process. Furthermore, the classification results highlight the ability of the model to discriminate between different types of diseases affecting rice leaves. This study demonstrates the effectiveness of our proposed method and positions it as a valuable tool for leaf disease detection in rice. By providing faster and more accurate control measures, our approach has the potential to significantly improve agricultural productivity. The successful application of the CNN model using MobilenetV3Large highlights its adaptability and robust performance in addressing the pressing problem of rice leaf diseases and provides a promising path for future advances in precision agriculture.
Implementation of App Engine and Cloud Storage as REST API on Smart Farm Application
Authors: Azkiya Khoirul , Irsan Muhamad , Fathoni Muhammad Faris
Published: March 2024

Smart Farm is an agricultural application that uses machine learning and cloud computing technology to improve efficiency in the farming process. Technological advancement and sustainable agriculture are two essential aspects of supporting global food security. This research investigates the implementation of App Engine and Cloud Storage in developing REST API in Smart Farm applications. By utilizing cloud computing technology, such as App Engine, and cloud storage, such as Cloud Storage, we can create efficient solutions to monitor and manage agriculture better. This research implements an App Engine and Cloud Storage to develop a REST API that allows Smart Farm application users to access data and control farming devices efficiently. The authors designed, developed, and tested this system to ensure optimal performance and reliability in agricultural data collection and distribution. This method has several significant advantages. First, App Engine allows for easy scalability, ensuring the system can handle increased data demand without disruption. Secondly, Cloud Storage provides secure and scalable storage for agricultural data, which can be accessed from anywhere. This provides easy and quick access to critical data for farmers. Moreover, the use of cloud technology also reduces infrastructure and maintenance costs. The developed system integrates the App Engine and Cloud Storage with the Smart Farm application. The App Engine is a processing engine that receives user requests via the REST API, processes the required data, and provides appropriate responses. Like image data, farm data is stored and managed on Cloud Storage. Users can access this data through the Smart Farm app or other devices, enabling better farming monitoring and decision-making.
Comparison of Performance of K-Nearest Neighbors and Neural Network Algorithm in Bitcoin Price Prediction
Authors: Apriadi Eko Aziz , Sriyanto Sriyanto , Lestari Sri , Yusuf Irianto Suhendro
Published: March 2024

This research evaluates and compares the performance of two prediction methods, namely K-Nearest Neighbors (K-NN) and Neural Network, in the context of Bitcoin price prediction. Historical Bitcoin price data is used as input to train and test both algorithms. Experimental results show that the K-NN algorithm produces a Root Mean Square Error (RSME) of 389,770 and a Mean Absolute Error (MAE) of 89,261, while the Neural Network has an RSME of 614,825 and an MAE of 284,190. Performance comparison analysis shows that, on this dataset, K-NN has better performance in predicting Bitcoin prices compared to Neural Network. These findings provide important insights for the selection of crypto asset price prediction models, especially Bitcoin, in financial and investment environments
Prediction of Stunting in Toddlers Using Bagging and Random Forest Algorithms
Authors: Juwariyem Juwariyem , Sriyanto Sriyanto , Lestari Sri , Chairani Chairani
Published: March 2024

Stunting is a condition of failure to thrive in toddlers. This is caused by lack of nutrition over a long period of time, exposure to repeated infections, and lack of stimulation. This malnutrition condition is influenced by the mother's health during pregnancy, the health status of adolescents, as well as the economy and culture and the environment, such as sanitation and access to health services. To find out predictions of stunting, currently we still use a common method, namely Secondary Data Analysis, namely by conducting surveys and research to collect data regarding stunting. This data includes risk factors related to stunting, such as maternal nutritional status, child nutritional intake, access to health services, sanitation, and other socioeconomic factors. This secondary data analysis can provide an overview of the prevalence of stunting and the contributing factors. To overcome this, the right solution is needed, one solution that can be used is data mining techniques, where data mining can be used to carry out analysis and predictions for the future, and provide useful information for business or health needs. Based on this analysis, this research will use the Bagging method and Random Forest Algorithm to obtain the accuracy level of stunting predictions in toddlers. Bagging or Bootstrap Aggregation is an ensemble method that can improve classification by randomly combining classifications on the training dataset which can reduce variation and avoid overfitting. Random Forest is a powerful algorithm in machine learning that combines decisions from many independent decision trees to improve prediction performance and model stability. By combining the Bagging method and the Random Forest algorithm, it is hoped that it will be able to provide better stunting prediction results in toddlers. This research uses a dataset with a total of 10,001 data records, 7 attributes and 1 attribute class. Based on the test results using the Bagging method and the Random Forest algorithm in this research, the results obtained were class precision yes 91.72%, class recall yes 98.84%, class precision no 93.55%, class recall no 65.28%, and accuracy of 91.98%.
Comparison Of Naïve Bayes And Decision Trees In Determining The Best Manager Of Nurul Jadid Islamic Boarding School Based On Forward Selection
Authors: Dardiri Farhan
Published: March 2024

In an effort to find a solution for determining the best administrators, Islamic boarding school administrators try to determine the nominations for the best administrators using existing service data and knowledge. The process of determining nominations for the best administrators is less accurate, requiring computational methods to classify which administrators fall into the best category. In the context of data mining, classification is an important aspect. One of the classification models used is Naïve Bayes which focuses on class probability, and Decision Tree C4.5 which produces a decision tree to determine the priority of indicators that are most influential in predicting the best management. Both of these algorithms have their respective advantages. This research aims to analyze and compare the performance of the Naïve Bayes and Decision Tree classification algorithms. The comparative results of testing the Naïve Bayes and C4.5 algorithms in determining the nominations for the best administrators at the Nurul Jadid Paiton Probolinggo Islamic Boarding School on 455 administrator data tested in this study show that there is a fairly large comparison of accuracy. Naïve Bayes with Forward Selection has an accuracy rate of 91.21%, higher than Naïve Bayes itself whose accuracy results are only 87.64%. there is a difference of 3.57%. Likewise, the accuracy of C4.5 with Forward Selection has an accuracy rate of 90.99%, higher than C4.5 alone which has an accuracy rate of 90.11%. there is a difference of 0.88%. So in the comparison between 4 algorithm model trials, Naïve Bayes and Forward Selection had the most dominant accuracy with an accuracy result of 91.21%.
Hybrid Analysis of Road Service Level Determination Decision Support System
Authors: Guswandi Dodi , Chairi Maiyozzi
Published: March 2024

Traffic congestion on the highway is one of the problems often faced by road users, as on the highway traffic in the city of Padang often experiences congestion due to the growth of several highways that are relatively smaller than the growth of traffic volume, causing a decrease in road service levels. This study aimed to determine the level of road services in several traffic lanes in the city of Padang. Data analysis using Hybrid Decision Support System (HDSS) modeling by combining the Analytical Hierarchy Process (AHP) method with the Weighted Aggregated Sum Product Assessment (WASPAS) This method is included in the Multi-Criteria Decision Making (MCDM) group. The AHP method can consistently determine the weight value of each criterion, and the WASPAS method can analyze alternative data to obtain decision results by ranking with the Weighted Sum Model (WSM) and Weighted Product Model (WPM) processes. The ranking results show that there are three types of road services in the city of Padang, namely B, C, and D with an accuracy rate of 0.6947%, this the results of this study using HDSS modeling can provide a better analysis process.
Classification of types Roasted Coffee Beans using Convolutional Neural Network Method
Authors: Metha Halifa Sekar , Kusrini Kusrini , Ariatmanto Dhani
Published: March 2024

In the current digital era, the role of technology in the agricultural industry is very necessary to increase yields which can have an impact on the productivity and welfare of farmers. Coffee is a drink that has been very popular for many years. Due to the high demand for coffee beans, this research aims to develop a system that can classify types of roasted coffee beans based on images using the Convolution Neural Network (CNN) method. Coffee bean processing is the most important stage in the coffee industry, classifying coffee beans often requires more in-depth knowledge and extensive experience regarding coffee beans. Therefore, this system can be a more effective solution. The author collects a dataset containing types of roasted coffee beans, then the Convolutional Neural Network  (CNN) can analyze in the form of visual patterns each type of coffee bean. This implementation is expected to help the coffee industry identify coffee beans quickly and accurately.