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All Journal International Journal of Electrical and Computer Engineering Jurnal INKOM Jurnal Edukasi dan Penelitian Informatika (JEPIN) Scientific Journal of Informatics Sistemasi: Jurnal Sistem Informasi International Journal of Artificial Intelligence Research Jurnal Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Pilar Nusa Mandiri MUST: Journal of Mathematics Education, Science and Technology Techno Nusa Mandiri : Journal of Computing and Information Technology Techne : Jurnal Ilmiah Elektroteknika Jurnal Pertanian Agros Jurnal Informatika Universitas Pamulang Indonesian Physical Review INTI Nusa Mandiri International Journal of Economics, Business and Accounting Research (IJEBAR) JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Jurnal Pengabdian kepada Masyarakat Nusantara Insyst : Journal of Intelligent System and Computation Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)

Agus Subekti

Balai Pengkajian Teknologi Pertanian Kalimantan Barat Jalan Budi Utomo No. 45, Siantan Hulu, Kotak Pos 6150, Pontianak 78061, Kalimantan Barat, Indonesia

Author-ID : 387109

Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Mathematics Physics Public Health Social Sciences Other

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PEMODELAN PREDIKTIF KONSUMSI ENERGI BANGUNAN GEDUNG KOMERSIAL DENGAN ALGORITMA SUPPORT VECTOR MACHINE Indriyanti, Indriyanti; Subekti, Agus
Jurnal Pilar Nusa Mandiri Vol 14 No 2 (2018): Pilar Nusa Mandiri : Journal of Computing and Information System Periode Septemb
Publisher : LPPM Universitas Nusa Mandiri

Konsumsi energi bangunan yang semakin meningkat mendorong para peneliti untuk membangun sebuah model prediksi dengan menerapkan metode machine learning, namun masih belum diketahui model yang paling akurat. Model prediktif untuk konsumsi energi bangunan komersial penting untuk konservasi energi. Dengan menggunakan model yang tepat, kita dapat membuat desain bangunan yang lebih efisien dalam penggunaan energi. Dalam tulisan ini, kami mengusulkan model prediktif berdasarkan metode pembelajaran mesin untuk mendapatkan model terbaik dalam memprediksi total konsumsi energi. Algoritma yang digunakan yaitu SMOreg dan LibSVM dari kelas Support Vector Machine, kemudian untuk evaluasi model berdasarkan nilai Mean Absolute Error dan Root Mean Square Error. Dengan menggunakan dataset publik yang tersedia, kami mengembangkan model berdasarkan pada mesin vektor pendukung untuk regresi. Hasil pengujian kedua algoritma tersebut diketahui bahwa algoritma SMOreg memiliki akurasi lebih baik karena memiliki nilai MAE dan RMSE sebesar 4,70 dan 10,15, sedangkan untuk model LibSVM memiliki nilai MAE dan RMSE sebesar 9,37 dan 14,45. Kami mengusulkan metode berdasarkan algoritma SMOreg karena kinerjanya lebih baik.

PENERAPAN SUPPORT VECTOR MACHINE (SVM) PADA SMALL DATASET UNTUK DETEKSI DINI GANGGUAN AUTISME Sugara, Bayu; Subekti, Agus
Jurnal Pilar Nusa Mandiri Vol 15 No 2 (2019): Pilar Nusa Mandiri : Journal of Computing and Information System Periode Septemb
Publisher : LPPM Universitas Nusa Mandiri

Seiring dengan perkembangan ilmu pengetahuan dan teknologi informasi, kehadiran machine learning dibidang komputer telah menjadi salah satu tren dan menarik banyak perhatian. Penggunaan machine learning tidak terlepas dari penggunaan data dalam pembelajarannya. Data yang besar merupakan data yang sering digunakan dalam proses pembelajaran machine learning. Perkembangan machine learning yang sangat pesat dapat memungkinkan data yang besar cepat pula terakumulasi. Namun, jarang ditemukan machine learning menggunakan data yang kecil (small dataset) dalam proses pembelajarannya. Small dataset ini biasanya bersifat private yang diambil dari sebuah organisasi yang akan dijadikan objek penelitian seperti data bank, rumah sakit, pabrik dan perusahaan jasa. Dalam penelitian ini peneliti menggunakan algoritma Support Vector Machine dan k-fold corss validation untuk menguji nilai keakuratan small dataset serta menggunakan teknik ensemble untuk mengetahui seberapa pengaruhnya teknik ensemble terhadap algoritma Support Vector Machine. Hasil dari penelitian ini menunjukkan bahwan teknik ensemble dapat meningkatkan performa akurasi pada Support Vector Machine. Model algoritma SVM dan teknik ensemble dengan poly kernel menunjukkan nilai akurasi terbaik yaitu sebesar 91%.

PREDICTION OF GLUCOSE LEVEL IN DIABETICS WITH SUPPORT VECTOR REGRESSION Wulandari, Devi; Subekti, Agus
Jurnal Pilar Nusa Mandiri Vol 16 No 1 (2020): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

One of the common diabetes factors that people hear is that they consume too much or often consume sweet foods or drinks so that blood sugar in the human body increases. The times and increasingly sophisticated technology make it easier for someone to be able to predict a disease such as diabetes with machine learning techniques. Therefore, from the existing problems, a machine learning technique will be made in predicting glucose levels in diabetics. The aim is to predict glucose levels in diabetics and find the best algorithm from several comparison algorithms. The results of the experiments carried out by the support vector regression algorithm have a lower mean squared error value of 28.9480 compared to other comparative algorithms and visualize the error classification seen that Instance no 47 has a prediction of the highest plasma glucose value of 189.2305.

K-BEST SELECTION UNTUK MENINGKATKAN KINERJA ARTIFICIAL NEURAL NETWORK DALAM MEMPREDIKSI RANGE HARGA PONSEL Saelan, M. Rangga Ramadhan; Subekti, Agus
INTI Nusa Mandiri Vol. 19 No. 1 (2024): INTI Periode Agustus 2024
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i1.5554

Determining the price of a mobile phone that will be released to the market cannot be based on assumptions alone. This problem can be overcome by utilizing machine learning. In this study, what is predicted is not the exact price, but rather the price range of a cellphone based on the specifications that are its attributes. In machine learning, the Deep Learning ANN model will be used to predict the price range of a mobile phone. To understand the relationship between features and labels, the Univariate feature selection method SelectKBest is used which will calculate the correlation value between features and labels. In this study, the best performance was obtained from the ANN model with feature selection and hyperparameter tuning, the evaluation of performance metrics obtained the highest accuracy of 97.5%. Experiments were conducted by building several models to compare until there was one model that performed well in processing training and validation data. Model evaluation is presented using confusion metrics with various types of performance metrics: accuracy, precision, recall and f1-score. This study also aims to evaluate the effectiveness of the SelectKBest feature selection method in improving model accuracy and testing various hyperparameter configurations to obtain the best performance.

Tantangan Revolusi Industri 4.0 bagi Pendidikan di Indonesia Subekti, Agus
Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya 2019: Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (4903.426 KB)

PERUBAHAN REVOLUSIONER DI BERBAGAI SEKTORDunia berubah, cara kerja lama terdisrupsi tanpa bisa terelakkanAda pemain lama tak bisa mendeteksi ancaman di luar jangkauan pemikiran.Ada yang tak berdaya memilih untuk tidak menghadapinya.Ada yang bertengkar sendiri di dalam.Ada yang berinovasi menghadapi tantangan baru.[H. Subiakto, 2018]

Multi View Natural Network for Cross-Project Software Defect Prediction Setiawan, Boy; Subekti, Agus
Intelligent System and Computation Vol 7 No 1 (2025): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v7i1.436

Software Defect Prediction (SDP) plays a critical role in software engineering by enabling early identification of potentially defective modules, to assist developers and testers in prioritizing testing and inspection efforts to improve software quality and reliability. Driven by rapidly changing business requirements, defect prediction models have become increasingly essential in quality assurance workflows. Traditional approaches to SDP focused on Within-Project Defect Prediction (WPDP), where models are trained on historical data from the same project and effective under sufficient data conditions. This challenge motivates the adoption of Cross-Project Defect Prediction (CPDP), which leverages data from different projects. However, CPDP faces notable challenges including datasets distributional differences and class imbalance, which can degrade prediction performance and bias. To address these issues, recent studies have proposed data transformation, resampling, and domain adaptation techniques. In this study, we explore a multi-view learning approach using Neural Networks (NN) to enhance generalization and performance in CPDP scenarios. By leveraging multiple views of the same dataset—generated through concatenation of heterogeneous software metrics, imputation for missing values, normalization using Box-Cox transformation, and embedding-based feature transformation—we aim to construct a robust Multi-View Neural Network (MVNN). This architecture enables the integration of diverse information while mitigating the limitations of single-view learning in CPDP. Our method preserves more in-formation compared to conventional approaches that rely only on shared features. Experimental validation using benchmark SDP repositories demonstrates the competitiveness of our approach, offering improved performance over existing CPDP models and highlighting the potential of multi-view learning in defect prediction tasks.

Pendekatan Ensemble dan Voting untuk Prediksi Cacat pada Berbagai Proyek Perangkat Lunak Kirso, Kirso; Subekti, Agus
MUST: Journal of Mathematics Education, Science and Technology Vol 10 No 1 (2025)
Publisher : Universitas Muhammadiyah Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30651/must.v10i1.26548

This study conducted experiments using ensemble methods, hyperparameter tuning, and voting to improve software defect prediction across multiple projects using the Kamei dataset. Five machine learning models LightGBM, XGBoost, Random Forest, Extra Trees, and Gradient Boosting were applied to six projects: Bugzilla, Columba, JDT, Mozilla, Platform, and Postgres. Overall, the models demonstrated good performance when tested on datasets of projects with similar characteristics or strong relationships, such as Mozilla, JDT, and Platform, achieving accuracy and F1 scores above 80%. This indicates that defect patterns learned from one project can be effectively applied to similar projects. However, the models’ performance dropped significantly when predicting defects in the Bugzilla project from other projects, indicating notable differences in defect patterns or feature incompatibility. Differences in data distribution across projects remain a major challenge in CPDP. Therefore, domain adaptation techniques or feature transformation methods are needed to reduce inter-project differences, enabling the models to better recognize defect patterns across projects. Despite some improvements, data differences and class imbalance still limit prediction performance. Future research should address these challenges.

Penerapan Artificial Intelligence untuk Meningkatkan Efisiensi Pengelolaan Database Administrasi Remaja Masjid Andra, Muhammad Bagus; Hermaliani, Eni Heni; Subekti, Agus; Haris, Muhammad
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 6 No. 1 (2025): Jurnal Pengabdian kepada Masyarakat Nusantara Edisi Januari - Maret
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar

Menurut studi terkini, administrasi masjid yang dikelola oleh remaja masjid sering menghadapi masalah seperti alur kerja yang lambat, pencatatan yang buruk dan kesalahan pengetikan, serta ketidakmampuan untuk mengambil informasi dengan cara yang tepat waktu dan akurat. Bertujuan untuk menyelesaikan masalah ini, kegiatan pengabdian ini meninjau integrasi Artificial Intelligence (AI) dalam manajemen administrasi masjid. Kegiatan spesifik pada Pengabdian kepada Masyarakat (PkM) meliputi analisis kebutuhan, pengembangan perangkat lunak berbasis AI, sesi pelatihan untuk staf, dan evaluasi sistem manajemen. Temuan lebih lanjut menunjukkan adanya penerapan AI untuk pengambilan keputusan menaikkan akurasi, tepat guna dan kecepatan pengambilan informasi selama proses administrasi. Disimpulkan bahwa penggunaan teknologi canggih yang diperlukan untuk organisasi berbasis komunitas mendukung pertumbuhan produktivitas. Dengan cara yang sama, inisiatif ini menetapkan preseden bagi organisasi lain untuk menggunakan solusi teknologi guna meningkatkan operasi administrasi.

Evaluasi Modern Model Pembelajaran Mesin pada Dataset SEERA untuk Estimasi Upaya Perangkat Lunak Nufus, Fina Sifaul; Subekti, Agus
Jurnal Informatika Universitas Pamulang Vol 10 No 2 (2025): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jiup.v10i2.51687

Estimating software development effort is crucial in project planning and management, especially in resource-constrained environments. This study piloted four modern regression models: Random Forest, Support Vector Machine (SVM), Lasso Regression, and Ridge Regression, chosen because they represent different approaches: ensemble, margin-based, and L1 and L2 regularization. Experiments were conducted using the SEERA (Software Effort Estimation with Real Attributes) dataset, consisting of 99 entries, with a modern Python pipeline including preprocessing, feature selection, Z-score normalization, data splitting (80:20), and cross-validation (5-Fold Cross Validation). Models were evaluated using MAE, RMSE, and R². Results showed that Random Forest outperformed both the 80:20 split (R² = 0.740, MAE = 3981.53) and K-Fold (R² = 0.715, MAE = 3152.03), while SVM performed the worst with a negative R². Lasso and Ridge are only competitive at 80:20 but significantly decrease on K-Fold, indicating less stability. This research contributes by providing an in-depth evaluation based on a single dataset and demonstrating that the transparent Python pipeline based on K-Fold can be replicated to improve estimation accuracy. Future research could be conducted using advanced ensemble methods (e.g., XGBoost) and evaluated on larger datasets to generalize the results.

A LIGHTWEIGHT AND PRACTICAL PIPELINE FOR CROSS-PROJECT DEFECT PREDICTION USING METRIC-BASED LEARNING Heriyani, Novia; Subekti, Agus
Jurnal Techno Nusa Mandiri Vol. 20 No. 2 (2025): Techno Nusa Mandiri : Journal of Computing and Information Technology Period o
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v20i2.6854

Cross-Project Defect Prediction (CPDP) addresses the scarcity of defect data in new software projects by transferring knowledge from existing ones. However, domain shift between projects remains a major challenge. This study introduces a lightweight and practical CPDP pipeline based on traditional metric features, integrating domain adaptation (CORAL, TCA, TCA+), feature selection, and resampling techniques. Through 120 configurations evaluated on multiple PROMISE datasets, we found that combining TCA or TCA+ with Synthetic Minority Over-sampling Technique combined with Edited Nearest Neighbors (SMOTEENN) consistently improved F1-Score and Recall on imbalanced datasets. LightGBM demonstrated the most stable performance across projects, while Logistic Regression yielded the highest MCC in specific cases. Principal Component Analysis (PCA) visualizations supported the effectiveness of domain alignment. The proposed pipeline offers a reproducible, cost-efficient alternative to deep learning models and provides actionable insights for defect prediction in resource-constrained environments.

Co-Authors A.A. Ketut Agung Cahyawan W Abdullah Umar, Abdullah Aditya, Dedy Yusuf Agung Tjahjo Nugroho Akhmad Syukron Andra, Muhammad Bagus Ari Setiawan Arumjeni Mitayani Aryono Adhi, Aryono BUDIYONO Cahyono, Bowo Eko Chaeriah Bin Ali Wael, Chaeriah Bin Dayat Kurniawan Devi Wulandari Diantika, Sri Edy Supriyanto Emy Setyaningsih Eni Heni Hermaliani, Eni Heni Faisal, Anas Fatah, Haerul Filda Angellia Heriyani, Novia Hidayat Teguh Wiyono Hiya Nalatissifa Indriyanti Indriyanti, Indriyanti Kahar Muzakhar Khultsum, Umi Kirso, Kirso Kusmayadi, Kusmayadi Kusumah, Maulana S. Lukman, Arif M. Rangga Ramadhan Saelan Mardiana, Vita Awalia Mareanus Lase Marta Dinata, Mochamad Mardi MUHAMMAD HARIS Muhammad, Nawafil Nasrullah Armi Nita Merlina, Nita Nufus, Fina Sifaul Nurkahfi, Galih Nugraha Nursulistyono, Yuda Nurul Priyantari Prasetya, Dwi Sabda Budi Purwoko Adhi Rahmadya Trias Handayanto Rian Hidayat Satyawan, Arief Suryadi Setiawan, Boy Siti Helmyati Sugara, Bayu Suprihati Supriyadi Supriyadi Suryanto, Wiwit Suyoto Suyoto Wahyudi Wahyudi Winarsa, Rudju

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