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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Masyarakat Informatika MATEMATIKA JURNAL SISTEM INFORMASI BISNIS Journal of Informatics and Technology Jurnal Sains dan Teknologi Semantik TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Penelitian Pendidikan IPA (JPPIPA) ComTech: Computer, Mathematics and Engineering Applications Jurnal Guru Dikmen & Diksus Indian Journal of Forensic Medicine & Toxicology Journal of Social Research Engagement: Jurnal Pengabdian Kepada Masyarakat
Aris Sugiharto
Program Studi Ilmu Komputer, Universitas Diponegoro, Semarang
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mathematics Physics Public Health Social Sciences Other

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Machine Learning Methods for Academic Achievement Prediction: A Bibliometric Review Nugraha, Fajar; Widowati, Widowati; Sugiharto, Aris
Jurnal Sistem Informasi Bisnis Vol 15, No 2 (2025): Volume 15 Number 2 Year 2025
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/vol15iss2pp221-226

Abstract

This study examines research trends regarding the prediction of academic achievement using machine learning. Research in the field of academic achievement is currently continuing to develop, but has not been explored comprehensively in a bibliometric context. The visualization provided includes a map of publication development using machine learning methods based on country, analysis of bibliographic pairs and keywords used. To find out the visualization results, bibliographic analysis was used using VOSviewer. The data used in this analysis were 76 articles collected from the Scopus database from 2018-2023. From the results of the analysis, it is known that research related to academic achievement still shows a growing trend in publications in the field of discussion of factors or predictors that influence academic achievement as well as research that proposes or evaluates models for predicting academic achievement. The research results show that although machine learning techniques such as Random Forest and Support Vector Machine are often used in academic achievement prediction research. Future research could consider developing a more adaptive and comprehensive approach regarding the contribution of specific factors that influence the accuracy of more in-depth prediction models in this field.
Comparative Analysis of Reconciliation Techniques: Bottom-Up, Top-Down, and MinT for Product Forecasting in Retail SMEs Rambing, Danni; Kusumaningrum, Retno; Sugiharto, Aris
ComTech: Computer, Mathematics and Engineering Applications Vol. 16 No. 1 (2025): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v16i1.12293

Abstract

Small and Medium Enterprises (SMEs) have experienced rapid growth, contributing approximately 95% to the global economy, 60% to global employment, and 50% to global GDP. This growth is accompanied by significant challenges, with approximately 70% of SMEs failing within the first three years, primarily due to poor inventory management. It emphasizes the crucial role of accurate demand forecasting for SMEs, particularly in the retail sector, where time series at various levels of hierarchical structure exhibit different scales and display diverse patterns. However, most existing research on demand forecasting for SMEs focuses on a single hierarchical level—either bottom, middle, or top—without addressing the entire hierarchy. The research sought to address this gap by forecasting across all hierarchical levels and evaluating different reconciliation techniques to generate coherent and accurate forecasts for multiple products in retail SMEs. The ETS state space model was used as the base forecasting model. This model was widely recognized as a benchmark in forecasting competitions. The reconciliation methods assessed were Bottom-Up, Top-Down based on historical proportions (average proportions), Top-Down based on forecast proportions, and Minimum Trace (MinT) (Ordinary Least Squares (OLS), OLS Non-Negative (OLS Non-Neg), Weighted Least Squares (WLS), and WLS Non-Negative (WLS Non-Neg)). The evaluation results show that the OLS Non-Negative method, with an average SMAPE value of 35.335%, produces more accurate reconciliation than other methods. In addition, this method also outperforms the base model with an increase in accuracy of 13%.
BERT Model Fine-tuned for Scientific Document Classification and Recommendation Antariksa, Muhammad Deagama Surya; Sugiharto, Aris; Surarso, Bayu
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6789

Abstract

The increasing number of academic documents requires efficient and accurate classification and recommendation systems to assist in retrieving relevant information. This system is built using the "bert-base-uncased” model from Hugging Face, which has been fine-tuned to improve the classification accuracy and relevance of document recommendations. The dataset used consists of 2.000 academic documents in the field of computer science, with features including titles, abstracts, and keywords, which were combined into a single input for the model. Document similarity is measured using cosine similarity, resulting in recommendations based on semantic proximity. Unlike traditional approaches, which rely primarily on word frequency or surface-level matching, the proposed method leverages BERT’s contextual embeddings to capture deeper semantic meanings and relationships between documents. This allows for more accurate classification and more context-aware recommendations. Evaluation results show that the best model configuration (learning rate 3e-5, batch size 32, optimizer AdamW) achieved 89.5% training accuracy and an F1-score of 0.8947, while testing yielded 91% accuracy and 90% F1-score. The recommendation system consistently produced Precision@k values above 92% for k between 5 and 30, with Recall@k reaching 1.0 as k increased. These results indicate that the system not only performs reliably in classifying complex academic texts but also effectively recommends contextually relevant documents. This integrated approach shows strong potential for enhancing academic document retrieval and supports the development of semantically aware information management systems.
Pengembangan Aplikasi Analisis PLS-SEM berbasis R Shiny dan Penerapan UTAUT2 untuk Evaluasi Penerimaan Sistem Informasi Fajar, Fajar Hari Prasetyo; Budi Warsito; Aris Sugiharto
JST (Jurnal Sains dan Teknologi) Vol. 13 No. 1 (2024): April
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jstundiksha.v13i1.68568

Abstract

Aplikasi yang digunakan untuk analisis data cenderung berbayar dan kurang efisien karena tidak menampilkan hasil evaluasi dalam satu laporan seperti aplikasi SmartPLS. Evaluasi yang dilakukan pada SIA XYZ sebelumnya belum memberikan hasil mendalam karena hanya menilai kepuasan pengguna pada SIA Universitas XYZ menggunakan model WebQual 4.0 dan analisis Regresi Linear Berganda. Oleh sebab itu diperlukan evaluasi lebih lanjut seperti Penerimaan Sistem Informasi. Penelitian ini bertujuan untuk mengembangkan aplikasi analisis Partial Least Square-Structural Equation Modeling (PLS-SEM) dengan R Shiny sebagai alternatif selain aplikasi konvensional dan Penerapan Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) untuk evaluasi penerimaan sistem pada SIA Universitas XYZ. Jenis penelitian ini adalah Mixed Method atau kombinasi, Pengembangan aplikasi menggunakan pendekatan Prototyping sedangkan analisis data menggunakan pendekatan Partial Least Square Structural Equation Modeling (PLS-SEM) dengan 102 sampel data kuesioner. Hasil penelitian menunjukkan bahwa, Pengembangan aplikasi analisis PLS-SEM bernama SEMRS berfungsi dengan baik dan dapat digunakan untuk analisis PLS-SEM secara gratis dan lebih efisien. Penerapan UTAUT2 dan analisis PLS-SEM menunjukan bahwa faktor yang mempengaruhi penerimaan SIA adalah kemudahan, suatu kondisi yang memfasilitasi, sikap nyaman dan senang, serta kebiasaan dalam menggunakan sistem. sedangkan pengaruh sosial tidak mempunyai pengaruh signifikan terhadap penerimaan sistem. Penelitian ini memberikan hasil evaluasi yang lebih dalam tentang faktor-faktor yang mempengaruhi penerimaan pengguna Sistem Informasi Akademik Universitas XYZ. Kesimpulannya yaitu aplikasi analisis Partial Least Square Structural Equation Modeling berbasis R Shiny (SEMRS) dapat digunakan sebagai alternatif selain aplikasi konvensional seperti SmartPLS secara gratis dan efisien.
Comparison Analysis of Nearest Road Calculations on Dijkstra Algorithm and A*(A-Star) Algorithm for Mapping BTS Tower Area Hidayat, Agung Rahmad; Gernowo, Rahmat; Sugiharto, Aris
Journal of Social Research Vol. 2 No. 10 (2023): Journal of Social Research
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/josr.v2i10.1391

Abstract

BTS (Base Transceiver Station) is a telecommunications infrastructure in the form of a tower with a transmitting antenna that facilitates wireless communication between communication devices and operator networks. BTS as a signal receiver and transmitter, its existence must be known to a user, such as maintenance staff and BTS tower operation staff to deal with existing problems. So a special system is needed for users to use in determining the closest path between the user and the location of the BTS tower. The purpose of this study is to make it easier for the user to determine the shortest path to the intended BTS tower location and to analyze the Dijkstra algorithm and the A* algorithm in determining the shortest route between the user's location and the location of the Telkomsel BTS tower in Semarang, Central Java. The results obtained from the 82 test data tested with the information system created show that Dijkstra's algorithm is more efficient than the A* algorithm. Validation is carried out by calculating with the system between the user's location and 82 test data or the location of BTS towers in the city of Semarang. The results of the validation carried out explained that the system was running according to the functions made and the results of the calculations carried out by the system were appropriate.
Optimizing Machine Learning Models for Anomaly-based IDS using Intercorrelation Threshold Wahyu Adi, Prajanto; Sugiharto, Aris; Malik Hakim, Muhammad; Rizki Saputra, Naufal; Hanif Setiawan, Syariful
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3355

Abstract

This study aims to improve the performance of attack detection on the Bot-IoT dataset that faces class imbalance. The method used involves developing a feature selection model based on the Pearson correlation coefficient between features, with an adaptive threshold applied. The datasets used consist of two types: D1, with the 10 best features, and D2, with all features. The oversampling technique is applied to the minority class, followed by calculating feature correlations to determine the best feature using a threshold based on the average of the highest and lowest correlations. The feature selection process is carried out iteratively, with performance testing across several machine learning algorithms, including KNN, Random Forest, Logistic Regression, and SVM. The results show that the proposed feature selection method can improve the performance of the minority class without sacrificing the majority class's performance. On the D1 dataset, the Random Forest algorithm achieved 96% accuracy, while KNN achieved 93%. On the D2 dataset, KNN achieved balanced performance, with average precision, recall, and F1-score of 0.99 for both classes, while Random Forest achieved lower results on the minority class. The implications of this study indicate that correlation-based feature selection can improve attack detection performance on datasets with high class imbalance, and it can be implemented in future studies to address similar problems in IoT-based intrusion detection systems.
Comparative Analysis of User Satisfaction of End User Computing Satisfaction, DeLone & McLean and Webqual 4.0 Methods Prastio, Wahyu Tedi; Farikhin; Sugiharto, Aris
Jurnal Penelitian Pendidikan IPA Vol 10 No 9 (2024): September
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i9.8484

Abstract

This study aims to analyze the level of user satisfaction of the SIAP Undip Mobile Application version 2.1.9 using three evaluation methods: End User Computing Satisfaction (EUCS), Delone and McLean, and Webqual 4.0. The study involved 100 Diponegoro University student respondents who used the application. Data was collected through a questionnaire distributed via Google Form and analyzed with SmartPLS 4.0 software to test validity, reliability, and research hypotheses. In this study, there were 11 hypotheses tested with three models. In the EUCS model, one hypothesis is accepted, namely Format has a significant effect on user satisfaction, while the other four hypotheses are rejected. In the Delone and McLean model, two hypotheses were accepted (Information Quality and System Quality) and one hypothesis was rejected (Service Quality). In the Webqual 4.0 model, one hypothesis is accepted (Service Interaction Quality) and two hypotheses are rejected (Information Quality and Usability Quality). The results of this study also provide suggestions for improvement for the development of the SIAP Undip version 2.1.9 application.
Co-Authors Abd. Rasyid Syamsuri Adi Wibowo Adieb, M. Risqi Amirul Adiputera, Yusuf Fahmi Afry Rachmat Agus Suwandono Andi Gunawan Antariksa, Muhammad Deagama Surya Ari Wibawa Budi Santosa Arief Hidayat Arif Wibawa, Helmie Arkan, Tsaqif Muhammad Ary Setyadi Bagoes Widjanarko Baihaqi, Muhamad Nur Bayu Surarso Budi Warsito Budi Warsito Dedy Kurniawan Hadi Putra, Dedy Kurniawan Hadi Didit Suprihanto, Didit Eko Adi Sarwoko Eko Didik Widianto Eko Didik Widianto Eko Nur Hidayat Eko Prasetiawan Fajar Hari Prasetyo Fajar Nugraha Ganis Khufad Arridho Hanif Setiawan, Syariful Helmi Arif Wibawa Helmie Arif Wibawa Helmie Arif Wibawa Henny Indriyawati Hidayat, Agung Rahmad Ikhthison Mekongga Indriyati Indriyati Irfan Pradipta Juwanda, Farikhin Kamal Maulana Kushartantya Kushartantya Kusworo Adi Lusiana Kristiyanti Lutfi Rinanto Mochammad Hosam Muhammad Malik Hakim, Muhammad Malik Mustafid Mustafid Nazla Nurmila, Nazla Nikmah Rahmawati Pradhitya Nur Diyah S Pramudita Eka Hananto Prastio, Wahyu Tedi Priyo Sidik Sasongko R Rizal Isnanto Ragil Saputra Ragil Saputra Rahmat Gernowo Rambing, Danni Riyana Putri, Fayza Nayla Rizki Saputra, Naufal Roby Hanintyo Nursio Sakti Rukun Santoso Satriyo Adhy Sembiring, Rinawati Septya Maharani, Septya Sinta Tridian Galih Sugiyamto Sugiyamto, Sugiyamto suhartono, Suahrtono Sukmawati Nur Endah Supriyono Supriyono Suryo Hartanto Sutikno Sutikno Sutopo Patria Jati Tantyoko, Henri Tarno Tarno Toni Prahasto Victor Gayuh Utomo Wahyu Adi, Prajanto Wahyu Krisna Hidayat Wahyu Krisna Hidayat Wahyudi Setiawan widowati widowati Wijayanto, Ahmad Yudie Irawan Yulianto Prabowo