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All Journal Journal of Mechanical Engineering Education (Jurnal Pendidikan Teknik Mesin) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Peternakan Nusantara EDUMATIC: Jurnal Pendidikan Informatika Jusikom: Jurnal Sistem Informasi Ilmu Komputer Jurnal Teknologi Dan Sistem Informasi Bisnis JIPIS Jurnal Informatika Ekonomi Bisnis JATI (Jurnal Mahasiswa Teknik Informatika) REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Jurnal Teknologi Informatika dan Komputer Bulletin of Computer Science Research Brilliance: Research of Artificial Intelligence Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Lentera Pengabdian VIDHEAS: Jurnal Nasional Abdimas Multidisiplin
muhidin, asep
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Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Mathematics Mechanical Engineering Social Sciences Veterinary Other

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Implementasi Sistem Basis Data Terintegrasi pada Aplikasi Dashboard Monitoring Replikasi Bank Tabungan Negara Metode Prototype: Implementation of an Integrated Database System in the Bank Tabungan Negara Replication Monitoring Dashboard Application Using the Prototype Method Ardhi, Hendra Yunianto; Muhidin, Asep; Wiyatno, Tri Ngudi
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.2049

Abstract

Dalam industri perbankan, ketersediaan dan perlindungan data menjadi aspek krusial. Bank Tabungan Negara (BTN) memerlukan sistem yang mampu memantau proses replikasi data secara real-time. Penelitian ini bertujuan untuk merancang dan mengimplementasikan Aplikasi Dashboard Monitoring Replikasi data menggunakan metode prototyping. Sistem ini menyediakan informasi status replikasi, kinerja, dan peringatan kesalahan secara terintegrasi. Metodologi yang digunakan meliputi analisis kebutuhan, perancangan sistem menggunakan flowchart dan diagram Unified Modeling Language (UML), serta pengembangan sistem secara iteratif dengan melibatkan pengguna. Hasil pengujian sistem dilakukan melalui User Acceptance Testing (UAT) yang melibatkan analis sistem dan tim quality control. Nilai rata-rata pada skala likert 1–5 menunjukkan skor tinggi antara 4,3 hingga 4,7. Aplikasi membantu tim IT mendeteksi keterlambatan replikasi, kesalahan sistem, serta mempercepat waktu tanggap dalam situasi darurat. Kesimpulannya, implementasi dashboard ini mampu meningkatkan efisiensi operasional, mengurangi risiko kehilangan data, dan mendukung pengambilan keputusan strategis. Namun, sistem ini masih dapat dikembangkan lebih lanjut dengan fitur notifikasi otomatis dan visualisasi interaktif.
Application of the K-Nearest Neighbor Machine Learning Algorithm to Preduct Sales of Best-Selling Products Danny, Muhtajuddin; Muhidin, Asep; Jamal, Akhiratul
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.4063

Abstract

The development of increasingly intense competition in the business world, accompanied by advances in information technology, has brought retail companies into a situation of tighter and more open competition. PT LG Innotek Indonesia is the only company that produces tuners in Indonesia. Looking at consumer demand, PT LG Innotek must improve product quality, and add products that consumers like and frequently purchase. For this reason, PT LG Innotek Indonesia needs an analysis that can help the company identify products that tend to sell well. This analysis can be carried out through the application of machine learning algorithms, especially the K-Nearest Neighbor method. The aim of this research is to find out how the KNN algorithm performs in predicting products that are selling well and not selling well at PT LG Innotek Indonesia. Based on the analysis results, prediction results were obtained with an accuracy level of 94.74% and an error rate of 5.26%. With this high level of accuracy and low error rate, it can be concluded that the K-Nearest Neighbor method is effectively used to predict sales of PT LG Innotek Indonesia's best-selling products.
Analisis Prediksi Resiko Diabetes Tahap Awal Menggunakan Algoritma Naive Bayes Danny, Muhtajuddin; Muhidin, Asep
Jurnal Teknologi Informatika dan Komputer Vol. 9 No. 2 (2023): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v9i2.2017

Abstract

Diabetes merupakan salah satu penyakit kronis yang diakibatkan adanya kelainan sekresi insulin pada kenaikan glukosa secara tidak teratur. Resiko penyakit stroke, penyakit jantung, kebutaan bahkan hingga resiko kematian merupakan penyakit komplikasi yang terjadi ketika adanya peningkatakn gula darah dalam tubuh pada penderita diabetes. Diabetes merupakan salah satu penyakit yang memiliki faktor resiko kematian yang tinggi. Deteksi dini penyakit diabetes perlu dilakukan sebagai upaya dalam menurukan tingkat kematian yang diakibatkan oleh faktor penyakit tersebut. Model yang diusulkan yaitu menerapkan algoritma Naive Bayes sebagai algoritma pengklasifikasi. Dataset yang dijadikan sebagai objek penelitian yaitu dataset Early Stage Diabetes Risk Prediction merupakan dataset terbuka yang bersumber dari UCI Machine Learning. Metode-metode yang digunakan dalam melakukan prediksi yaitu metode data mining. Data mining merupakan serangkaian tindakan untuk menemukan hubungan dari pola dan kecenderungan dari data yang disimpan. Desain alur sistem klasifikasi jenis pada penelitian ini, dimulai dari penentuan Dataset, Loading dan baca data, Analisis Eksplorasi Data, Data Preprocessing, membangun model data, evaluasi Confusion Matrix, dan Hyperparameter Tuning. Didapatkan nilai True Positive sebanyak 276, True Negative sebanyak 180, False Positive sebanyak 20 dan False Negative sebanyak 44. Nilai akurasi yang didapatkan dalam penelitian yaitu sebesar 87.88% dengan kategori Good Classification serta memiliki error rate yang rendah yaitu 12.12% termasuk kedalam kategori Good Error Rate. Hasil penelitian tersebut menunjukan bahwa algoritma Naive Bayes memiliki kinerja yang baik serta dapat dijadikan sebagai landasan dalam memprediksi risiko diabetes tahap awal.
Sistem Informasi Asset Management Di Pt. Sinar Sosro Dengan Metode Waterfall Berbasis Mobile Ramadhan, Aldi; Muhidin, Asep; Butsianto, Sufajar; Ermanto, Ermanto; Setyaningrum, Retno Purwani Setyaningrum
Jurnal SIGMA Vol 16 No 1 (2025): Juni 2025
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/sigma.v16i1.6053

Abstract

Perkembangan teknologi informasi telah mendorong transformasi digital di berbagai bidang, termasuk manajemen aset. Pengelolaan aset yang efektif menjadi krusial bagi perusahaan dan individu untuk memastikan akurasi data, optimalisasi sumber daya, dan pengambilan keputusan yang tepat. Namun, PT. Sinar Sosro masih menghadapi kendala dalam pengelolaan aset secara optimal. Untuk mengatasi hal ini, penelitian ini mengembangkan Aplikasi Asset Management berbasis Android menggunakan metode Waterfall. Aplikasi ini dirancang untuk menyimpan data aset perusahaan, memudahkan staf dalam mengakses informasi aset, serta mendukung pengelolaan yang efisien melalui perangkat mobile. Dengan antarmuka yang intuitif dan berbasis cloud, sistem ini dapat diakses secara fleksibel melalui smartphone, menjadikannya solusi praktis untuk meningkatkan akurasi dan kecepatan dalam manajemen aset.
Implementasi Metode Markerless Augmented Reality Sebagai Media Promosi Home Furnishing Berbasis Android: Implementation of Markerless Augmented Reality Method as an Android-based Home Furnishing Promotion Media Miyanti, Violi; Muhidin, Asep; Ardiatma, Dodit
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 1 (2024): MALCOM January 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i1.1019

Abstract

Tak bisa dipungkiri perkembangan smartphone yang begitu pesat membuat banyak aplikasi menggunakan Augmented Reality lebih menarik. Serta sedikitnya media promosi penjualan furniture menggunakan Augmented Reality. Implementasi Augmented Reality kedalam sebuah aplikasi Android bertujuan agar dapat meminimalisir permasalahan umum yang terjadi ketika membeli furniture. Teknologi Augmented Reality merupakan suatu teknologi yang menyatukan antara dunia nyata dan dunia maya dengan menggunakan perangkat keras yaitu kamera. Teknologi ini tidak sepenuhnya menggantikan sebuah realitas tetapi menambahkan beberapa benda-benda maya dalam bentuk 2 dimensi atau 3 dimensi kedalam lingkungan nyata 3 dimensi lalu ditampilkan secara real time atau waktu yang sebenarnya. Selama ini Augmented Reality diaplikasikan dengan menggunakan Marker atau yang lebih dikenal dengan metode Markerbase. Diantar dua metode Augmented Reality, metode Markerless Augmented Reality (MAR) adalah salah satu metode yang sedang berkembang saat ini. Metode ini membuat pengguna tidak lagi harus menggunakan marker untuk menampilkan elemen-elemen digital.
Optimasi Algoritma Random Forest untuk Prediksi Eksport Kelapa Sawit Global Danny, Muhtajuddin; Muhidin, Asep
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.744

Abstract

Palm oil production is a strategic commodity in global trade, with a trend showing an increase from year to year. This study aims to optimize the Random Forest algorithm in predicting the amount of global palm oil production based on historical data. The dataset used consists of 12,458 observations with one dependent variable (Palm_Oil_00002577_) representing the amount of palm oil production, and four independent variables: country, Code, Year, and Palm_Oil_00002577_log. The data is divided into 80% for training (9,966 observations) and 20% for testing (2,492 observations). The model optimization process is carried out by adjusting the key parameters of Random Forest using Grid Search and Cross-Validation. The initial Random Forest model (without optimization) produces a Root Mean Squared Error (RMSE) value of 115.27 and an R-squared (R²) value of 0.9824 on the test data. After optimization using Grid Search and Cross-Validation on key parameters (n_estimators, max_depth, and max_features), the optimized model showed significant performance improvements, with the RMSE decreasing to 103.54 and the R² increasing to 0.9984. The decrease in the RMSE indicates a reduction in the model's average prediction error, while the increase in R² approaching 1 indicates the model's ability to explain almost all of the variation in global palm oil production data. These results indicate that parameter optimization in Random Forest can substantially improve prediction accuracy, enabling the model to be used as a production planning tool and strategic decision-making tool in the palm oil commodity trading sector.
Prediksi Kegagalan Perangkat Industri Menggunakan Random Forest dan SMOTE untuk Pemeliharaan Preventif Muhidin, Asep; Muhtajuddin Danny; Surojudin, Nurhadi
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.745

Abstract

Preventive maintenance is an essential strategy to minimize losses due to industrial equipment failures. This study aims to develop an equipment failure prediction model using the Random Forest algorithm with the SMOTE technique to address class imbalance. The dataset used is the AI4I 2020 Predictive Maintenance Dataset with 10,000 entries and six main input variables. Preprocessing includes normalization of numerical features, one-hot encoding for categorical features, and handling of missing values. The Random Forest model was optimized using GridSearchCV and compared with K-Nearest Neighbors. Results show that Random Forest with SMOTE achieved 97% accuracy, 0.47 precision, 0.75 recall, and 0.58 F1-score on the failure class. This model outperforms KNN in detecting failures, particularly in imbalanced data. These findings contribute to the development of an early warning system to support preventive maintenance in industrial environments.
Development of an Integrated Web-Based Quality Control Dashboard for Automated Sorting Data Monitoring Yusmadi, Yudies; Pramudito, Dendy K; Muhidin, Asep
Jurnal Informatika Ekonomi Bisnis Vol. 7, No. 3 (September 2025)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v7i3.1268

Abstract

This study supports the development of a web-based Quality Control (QC) dashboard designed to improve the monitoring of product sorting data in manufacturing process. The impetus comes from the fact that traditional methods of capturing data by hand are very likely to be incorrect, slow, and cause problems with managing data. Currently, sorting results are still written down on physical log sheets which often leads to delays in reporting, puts records at risk of being lost or destroyed, and makes it harder to analyze data in real time. The study uses the Agile Development method to fix these problems, with a focus on iterative design, getting input from stakeholders, and making improvements all the time. The proposed dashboard will include several basic features, such as the ability to enter sorting results digitally, interactive data visualization in both graphical and tabular formats, automatic quality control report generation, and user management through role-based access control. These features are expected to turn data into useful information, which will allow the QC team to quickly make decisions based on evidence that enhance product quality. Web programming utilizes modern web technologies such as Laravel framework for backend processing, JavaScript, HTML, CSS, and Bootstrap to make responsive the user interface. Utilization of open-source technologies is meant to ensure that the system may grow and be maintained while keeping installation costs low.
GENRE BASED APPROACH FOR TECAHING ESL AND EFL WRITING: SYSTEMATIC LITERATURE REVIEW Forsia, Lastry; Muhidin, Asep
JIPIS Vol. 34 No. 2 (2025): Oktober 2025
Publisher : FKIP, UNIVERSITAS ISLAM SYEKH-YUSUF TANGERANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33592/jipis.v34i2.7978

Abstract

In the last decade, the usage of genre-based approaches (GBAs) has proliferated especially in the English education sector. This article presents an analysis of a systematic review based on published studies on the genre-based approach (GBA) in teaching writing. It aims synthesize current research findings on the implementation and effectiveness of GBA, to show how GBA enhances students’ writing skill, as well as the research trends related to GBA in teaching ESL/EFL writing. Using databases such as Scopus, ERIC, Francis & Taylor, google Schoolar and etc., studies published from 2019-2025 with a total of twenty studies. This review used thematic analysis to categorize the main themes that emerge in the selected studies. The review reveals that GBA significantly enhances learners’ genre awareness, textual organization, and communicative competence through explicit instruction and scaffold learning stages. However, it also identifies major challenges, including limited teacher knowledge of genre theory, lack of institutional support, and contextual constraints in exam-oriented curricula. Overall, this review confirms that GBA is an effective and contextually adaptable framework for developing writing proficiency in ESL/EFL classrooms, while also highlighting the need for further longitudinal and technology-integrated studies to strengthen its pedagogical application.
Co-Authors . Ermanto Abdul Halim Anshor Ahmad Syukron Ardhi, Hendra Yunianto Baehaqi Butsianto, Sufajar Dede Kardaya Deden Sudrajat Dendy K. Pramudito Dodit Ardiatma Dwi Prasetyo, Novian Edi Triwibowo Effendi, M. Makmun Eko Ardianto, Riski Eko Putra, Fibi Elkin Rilvani Ermanto Fikri, Chairul Forsia, Lastry Hidarini, Silviana Fitria Hutauruk, Basar Maringan Ibnu Mubarak Indah kurniawati, Indah Jamal, Akhiratul Latifah Nurhasanah, Reka Hani Miyanti, Violi Muhammad Noor, Ridwan Adam Muhtajuddin Danny Naya, Candra Nurhadi Surojudin Pamungkas, Panji Putra Purwani Setyaningrum, Retno Putra, Fibi Eko Ramadhan, Aldi Setyaningrum, Retno Purwani Setyaningrum Setyawan, Wisnu Siswandi, Arif Surojudin, Nurhadi Tri Ngudi Wiyatno Triwibowo, Edi Wisnu Setyawan Yusmadi, Yudies Zaenal Abidin, Zihan