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All Journal EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi ELSE (Elementary School Education Journal) : Jurnal Pendidikan dan Pembelajaran Sekolah Dasar Journal of Information Systems and Informatics Neo Teknika: Jurnal Ilmiah Teknologi Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Teknologi Informasi Cyberku Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Algoritma Majalah Ilmiah Inspiratif Indonesian Journal of Community Empowerment (IJCE) "JAMASTIKA" Jurnal Mahasiswa Teknik Informatika INOVTEK Polbeng - Seri Informatika Jurnal Ilmu Komputer
Abdul Rohman
Universitas Ngudi Waluyo
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Social Sciences

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Implementasi Algoritma Decision Tree C4.5 Untuk Prediksi Kepuasan Mahasiswa Terhadap Layanan Akademik Abdul Rohman; Agung Wibowo; Sabilatul Hidayah
Multimatrix Vol. 4 No. 1 (2022): Kemajuan Teknologi Informatika Pada Era Dunia Digital
Publisher : Universitas Ngudi Waluyo

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Abstract

Pelayanan akademik di perguruan tinggi sangat berpengaruh terhadap keberhasilan proses belajar mengajar. Pelayanan akademik menjadi suatu usaha yang dilakukan lembaga pendidikan terutama perguruan tinggi untuk memberikan kemudahan akan pemenuhan kebutuhan mahasiswa dalam kegiatan belajarnya. Maka diperlukan suatu informasi kepuasan mahasiswa terhadap layanan akademik untuk djadikan sebagai prediksi dan bahan kebijakan di lembaga pendidikan terutama di S1 Teknik Informatika Universitas Ngudi Waluyo. Dalam penelitian ini dilakukan pengolahan data kepuasan mahasiswa terhadap layanan akademik saat pembelajaran menggunakan metode blended learning disaat pandemi covis-19 dengan menerapkan algoritma decision tree C4.55. Hasil dalam penelitian ini adalah membuat pola dan rule keputusan dengan 4 rule/pola dan nilai akurasinya 89.17% dengan nilai AUC 0,70 dengan kategori klasifikasi cukup. Kata kunci: Algoritma C4.5, Prediksi, Kepuasan Mahasiswa, Decision Tree
Pengembangan Model Evaluasi Berbasis Sistem Menggunakan Moodle di Komunitas e-guru Semarang Abdul Rohman
Multimatrix Vol. 4 No. 2 (2022)
Publisher : Universitas Ngudi Waluyo

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Abstract

E-guru merupakan komunitas guru seluruh indonesia yang memiliki anggota 19.763 orang dari kalangan guru, dosen dan tenaga pengajar lainnya. Dalam pembelajaran, komponen evaluasi merupakan tahapan yang sangat penting untuk mengukur kemampuan siswa/mahasiswa dalam mempelajari materi yang disampaikan oleh pengajar dan evaluasi dijadikan sebagai umpan balik guru/dosen untuk mengembangan model pembelajaran yang sesuai kebutuhan masyarakat. Selain itu seorang guru dituntut untuk melek teknologi informasi dalam pengembangan evaluasi berbasis sistem terutama pemafaatan aplikasi/program moodle. Moodle merupakan aplikasi/program yang efektif dan efisien untuk melaksanakan pembelajaran secara blended learning terutama dalam pengembangan model evaluasi. Dengan adanya pengembangan model berbasis sistem menggunakan moodle bagi guru/dosen di komuniatas guru dalam bentuk pelatihan dan pendampingan, dapat memberikan pengetahun dan keterampilan dalam melaksanakan evaluasi berbasis digital. Kata kunci: Evaluasi, Sistem, Moodle
Implementasi Aplikasi Moodle Untuk E-Learning Bagi Guru-guru di Komunitas e-guru Semarang Abdul Rohman; Teguh Santoso
Multimatrix Vol. 5 No. 1 (2023): Jurnal Multimatrix Juli 2023
Publisher : Universitas Ngudi Waluyo

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Abstract

Komunitas e-guru.id Semarang merupakan komunitas yang memberikan kompetensi guru-guru indonesia dalam bentuk pelatihan, workshop, diklat dan lainnya. Akan tetapi masih banyak anggota/peserta komunitas e-guru.id yang belum mengetahui dan memiliki keterampilan dalam pembuatan e-lerning menggunakan aplikasi moodle untuk memberikan pembelajaran kepada siswa secara jarak jauh atau online. Maka diperlukan program pengabdian kepada masyarakat dalam bentuk pelatihan dan pendampingan mengenai mengimplementasikan aplikasi moodle untuk e-learning. Hasil dari program pengabdian kepada masyarakat ini memberikan kompetensi dalam penggunaan media pembelajaran e-learning kepada guru-guru indonesia dalam komunitas e-guru.id. Kata kunci: e-guru.id, e-learning, moodle
Komparasi Algoritma Machine Learning dan Ensemble Methods dalam Prediksi Penyakit Jantung dengan Dataset yang Bervariasi Abdul Rohman; Sri Mujiyono
Multimatrix Vol. 4 No. 2 (2022)
Publisher : Universitas Ngudi Waluyo

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Abstract

This study aims to compare the performance of various machine learning algorithms and ensemble methods in predicting heart disease, using two different datasets: datasets from the UCI Machine Learning Repository and Kaggle. Nine algorithms were tested, including Logistic Regression (LR), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), XGBoost, LightGBM, CatBoost, Support Vector Machine (SVM), and Naive Bayes (NB). The data were processed through data cleaning, normalization, and splitting the dataset into training and test data. The experimental results showed that K-Nearest Neighbors (KNN) performed best with an accuracy of 91.80%, followed by Support Vector Machine (SVM) and Random Forest (RF), which also demonstrated stable and effective results in handling complex datasets. Although Decision Tree (DT) and Naive Bayes (NB) performed lower, these results demonstrate that basic machine learning algorithms can provide adequate results for heart disease classification. This study recommends the use of ensemble algorithms and further exploration in feature engineering to improve predictions.
Analisis Kinerja Algoritma Machine Learning untuk Prediksi Penyakit Jantung Menggunakan Metode Data Preprocessing Terintegrasi Abdul Rohman; Sri Mujiyono
Multimatrix Vol. 5 No. 1 (2023): Jurnal Multimatrix Juli 2023
Publisher : Universitas Ngudi Waluyo

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Abstract

Penyakit jantung merupakan salah satu penyebab kematian tertinggi secara global, sehingga diperlukan metode prediksi yang akurat dan dapat dipercaya untuk mendukung deteksi dini. Penelitian ini bertujuan untuk menganalisis kinerja beberapa algoritma Machine Learning—Logistic Regression, Random Forest, Support Vector Machine (SVM) dengan kernel RBF, dan XGBoost—dalam memprediksi penyakit jantung menggunakan dataset Cleveland yang tersedia di platform Kaggle. Penelitian ini menggunakan pipeline preprocessing terintegrasi yang mencakup pembersihan data, transformasi data, reduksi data, serta pengujian dengan dua skenario: tanpa SMOTE dan dengan SMOTE untuk menangani ke kinerja kelas. Hasil penelitian menunjukkan bahwa Random Forest memberikan performa terbaik pada skenario tanpa SMOTE dengan akurasi 0.9016, recall 0.9643, F1-score 0.9000, dan ROC-AUC 0.9594. Sementara itu, penerapan SMOTE tidak secara signifikan meningkatkan akurasi, namun mampu menstabilkan recall dan F1-score pada beberapa algoritma, terutama Logistic Regression dan SVM. Secara keseluruhan, hasil eksperimen menegaskan bahwa kualitas preprocessing dan penanganan ke konsistensi kelas memiliki pengaruh utama terhadap kinerja model. Studi ini memberikan kontribusi pada penerapan praktik terbaik dalam pengembangan model prediksi penyakit jantung berbasis Machine Learning yang dapat direplikasi pada penelitian lanjutan maupun implementasi klinis. Kata kunci: Machine Learning, Prediksi Penyakit Jantung, Preprocessing Data, SMOTE, Random Forest, Regresi Logistik, SVM, XGBoost.
Implementation of Blended Learning Based on E-Learning in the e-guru.id Community Semarang Abdul Rohman; Purwosiwi Pandansari
Multimatrix Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Ngudi Waluyo

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Abstract

Digital transformation in education encourages teachers to master innovative learning technologies. Digital teacher communities such as e-guru.id play a strategic role in facilitating the development of digital pedagogical competencies through the implementation of blended learning. This study aims to analyze the implementation of e-learning-based blended learning in the e-guru.id Semarang community, identify the platforms and strategies used, and explore the challenges and their impact on teacher competence. The research employed a descriptive qualitative approach with data collection techniques through observation, in-depth interviews, and documentation of 30 teacher members of the e-guru.id Semarang community. Data analysis was conducted descriptively qualitatively using the Miles and Huberman interactive model. The results showed that the e-guru.id Semarang community implemented a blended learning model with a combination of synchronous learning through Google Meet and Zoom, as well as asynchronous learning using Google Classroom, Moodle, and WhatsApp as supporting media. The implementation of blended learning in this community proved effective in increasing teachers' digital pedagogical competence by 76%, expanding access to collaboration among members, and improving the ability to design technology-based learning. However, challenges faced include limited internet access (43%), digital literacy gaps (35%), and time constraints (22%). The e-guru.id Semarang community has successfully become a model of a professional learning community that supports the sustainable development of teachers in the digital era. Keywords: blended learning, e-learning, teacher community, e-guru.id, digital competence
Cyberbullying Detection in Indonesian TikTok Comments Using IndoBERT with Fairness Evaluation Hanik Dewi Jayanti; Abdul Rohman
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1448

Abstract

This study investigates automated cyberbullying detection on TikTok within the Indonesian digital context, where high social media usage among children and adolescents demands scalable and consistent content moderation. We propose an IndoBERT-based framework for detecting and classifying cyberbullying in Indonesian-language TikTok comments, incorporating algorithmic fairness considerations. A dataset of 2,122 TikTok comments was collected from a publicly available Kaggle repository and divided into training, validation, and testing sets using a 70:15:15 stratified sampling ratio. The IndoBERT-base-p1 model was fine-tuned with the PyTorch and HuggingFace frameworks, optimizing hyperparameters like the AdamW optimizer and learning rate scheduling. Experimental results show that the model achieved an accuracy of 70.66% and a ROC-AUC score of 0.7969, demonstrating solid discriminative power. With a macro F1-score of 0.7066 and a cyberbullying recall of 0.7170, the model shows balanced performance in identifying harmful content. A key contribution of this study is a fairness evaluation framework that reveals an accuracy gap of 2.08% and an equal opportunity gap of 0.0208, indicating overall fairness. However, demographic parity remains a concern. This system, supporting content triage combined with human review, enhances moderation workflows by filtering non-cyberbullying cases while flagging potentially harmful content for human oversight.
SENTIMENT ANALYSIS OF COMMENTS ON THE HASHTAG #BUBARKANDPR ON TWITTER USING THE NAÏVE BAYES METHOD Hendri Saputra; Abdul Rohman
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 1 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2026
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i1.26930

Abstract

The development of social media, particularly Twitter, has positioned it as a primary platform for expressing public opinion on political issues in Indonesia. One phenomenon that has attracted significant attention is the viral hashtag #BubarkanDPR, which reflects increasing public criticism of the performance of the legislative institution. Several previous studies have shown that the Naïve Bayes machine learning method performs well in sentiment classification tasks. A review of five relevant journals reveals varying accuracy levels: (1) tweet-based sentiment analysis on corruption achieved an accuracy of 82–87%, (2) sentiment analysis of anti-corruption campaigns reached 84%, (3) research on public sentiment toward the Corruption Eradication Commission (KPK) showed a Naïve Bayes accuracy of 82%, (4) a study on the revision of the KPK Law reported an accuracy of 78%, and (5) a comparative study of methods on corruption and tax issues recorded an accuracy of 80% for Naïve Bayes. These findings confirm that Naïve Bayes is consistently applied to political and sensitive topics with stable performance. This study examines public sentiment toward the hashtag #BubarkanDPR by applying the Naïve Bayes method. Data were collected through crawling Twitter comments and processed through several stages, including cleaning, case folding, tokenization, stopword removal, and stemming. The model was evaluated using a confusion matrix. The results show that the model achieved an accuracy rate of 77%, which is consistent with the accuracy range reported in several previous studies. Thus, Naïve Bayes is proven to be sufficiently effective in analyzing sentiment on dynamic and controversial political issues. This study provides insights into public perception and can serve as a reference for further research on social media–based public opinion analysis.
Co-Authors Afiatna, Puji Afifah Nur Hawa Agung Wibowo Agung Wibowo Anief Rufiyanto Aqmal Putra R.R Bayu Evendi Catur Supriyanto Choerul Fikri Hamdani Cicik Harini Dedy Alan Wirawan Desi Fitria Wulandika Dika Ade Royani Erika Ifalinda Fernanda Bagus Dwi Prastyo Fiki Wijayanti Fitria Nur Anifah Hanik Dewi Jayanti Hendri Saputra hia, Meriani Intan Febriyana Irene Trivena Montalili Irfan Fakhruddin Ismail, Ahmad Hafizh Iwan Setiawan Wibisono Khilda Afriyani Khoirul Asrofi Lismawati Gulo Mayasa Kurnia Muhammad Rochcham Munasiroh Munasiroh Nopita Widayanti Noviyana Dwi Herlinda Rochmawati, Nur Intan Sabilatul Hidayah Sri Mujiyono Sugeng Deri Pratama Tara Indah Afelia Teguh Santoso Ucik Lujiyanti Rukmana Umi Halimatussa'diyah Vincent Suhartono Wahyu Aji Kuswoyo Wildan Khoiri Priandana Yoannes Romando Sipayung Yusak Maryunianta