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All Journal STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Jurnal Widya International Journal Software Engineering and Computer Science (IJSECS)
Pandhu Pramarta
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Mathematics Social Sciences

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Sistem Informasi Berbasis Web Pada TK Islam Rabbani Jakarta Selatan Pandhu Pramarta
STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Vol 5, No 2 (2020)
Publisher : Universitas Indraprasta PGRI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (805.413 KB) | DOI: 10.30998/string.v5i2.8134

Abstract

In this era, it is demanded to know and understand the development of the importance of technology. Technology provides convenience in helping human activities, especially in the academic world. At the moment Rabbani Islamic Kindergarten has not used the website as a tool for publication media and information exchange, so the information cannot be accessed by public in real time. In this research web is designed and created using SDLC (System Development Life Cycle) Waterfall Model. The research objective is  designing and creating a website to facilitate easy access information, to cover wider area publication, and increase awareness. The results of this study are a website that helpful and easier for teachers, students and all stakeholders of the school to easily get information quickly and accurately. Referring to this, it is expected that by using a web-based information system, any information can be accessed and published to the people, making it easier for the public to get information at real time through an internet connection.
Penerapan Metode Forward Chaining Pada Sistem Informasi Pencatatan Gizi Balita Pada Posyandu Sutra 1 Pandhu Pramarta; Unknown Pujiastuti; Yossi Indrawati Syuhardi
Jurnal Widya Vol. 3 No. 2 (2022): Vol 3 No 2 (2022)
Publisher : Akademi Manajemen Informatika dan Komputer Widyaloka

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Abstract

Posyandu berada di tengah masyarakat dan memberikan pelayanan kesehatan kepada ibu dan balita. Posyandu mengadakan penimbangan bulanan balita untuk melacak data usia, berat badan dan tinggi badan. Informasi ini digunakan sebagai indikator untuk menilai status gizi, tinggi dan berat badan balita. Catatan ini digunakan untuk melihat balita yang masih kurus menurut standar penilaian gizi Antropometri. Menilai pemakanan dalam kanak-kanak kecil dengan antropometri dan pengiraan Zscore, melihat kategori ambang untuk menentukan keperluan. Penelitian ini berupaya membuat sistem informasi pencatatan gizi balita, dengan menggunakan metode Forward Chaining dan model SDLC, agar dapat membantu pengambil keputusan dalam mengambil tindakan terhadap balita yang tidak memenuhi tolok ukur gizi. Hasil penelitian ini mampu menampilkan status gizi balita secara cepat dan efektif, serta memberikan laporan penilaian yang tertata dengan baik.
Multiclass Electrical Fault Classification in Three-Phase Power Systems Using Random Forest, Support Vector Machine, and XGBoost Sri Mardiyati; Pandhu Pramarta
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 3 (2026): DECEMBER 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i3.8397

Abstract

Electrical fault classification in three-phase power systems remains challenging because different fault conditions can exhibit similar current and voltage characteristics, limiting the diagnostic capability of conventional binary fault detection. This study aimed to compare the performance of Random Forest, Support Vector Machine, and Extreme Gradient Boosting (XGBoost) for multiclass electrical fault classification and to identify the most effective and stable model. The publicly available Electrical Fault Detection and Classification dataset obtained from the Kaggle repository was used in this study. The dataset contains 7,861 observations with six electrical features comprising three-phase currents (Ia, Ib, and Ic) and voltages (Va, Vb, and Vc), which were used to classify six operating conditions: Normal, Line-to-Ground, Line-to-Line, Double Line-to-Ground, Three-Phase, and Three-Phase-to-Ground faults. The models were evaluated using accuracy, precision, recall, F1-score, and five-fold stratified cross-validation, complemented by confusion-matrix and feature-importance analyses. Random Forest achieved the best test performance with an accuracy of 86.78% and an F1-score of 86.76%, while cross-validation produced a mean accuracy of 87.55% and a mean F1-score of 87.53%. Class-level analysis revealed that Three-Phase and Three-Phase-to-Ground faults were the most difficult conditions to distinguish because of substantial overlap in their electrical characteristics. The findings demonstrated that Random Forest provided the most effective and stable classification performance and that combining performance, stability, and class-level analyses provided deeper insight into multiclass electrical fault diagnosis.
Co-Authors Sri Mardiyati Unknown Pujiastuti Yossi Indrawati Syuhardi