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All Journal Jurnal Momentum Jurnal Kajian Informasi & Perpustakaan Jurnal Manajemen Informatika dan Sistem Informasi Jurnal Teknologi Informatika dan Komputer Jurnal Teknik Informatika (JUTIF) Jurnal Abdi Masyarakat Indonesia Journal of Soft Computing Exploration
Arif Rohmadi
Universitas Sebelas Maret
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Library & Information Science Public Health

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CASE BASED REASONING UNTUK PEMILIHAN KEGIATAN ORGANISASI MAHASISWA Arif Rohmadi
Jurnal Momentum ISSN 1693-752X Vol 17, No 2 (2015): Volume 17 No. 2 Tahun 2015
Publisher : ITP Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (676.383 KB)

Abstract

Organisasi merupakan tempat untuk mengasah softskill mahasiswa. Lewat organisasi mahasiswa dapat belajar kepemimpinan, public speaking, kerjasama, diskusi, dan bersosialisasi, sehingga menjadi nilai tambah bagi mahasiswa ini sendiri. Permasalahan muncul ketika mahasiswa dihadapkan untuk memilih organisasi yang sebaiknya diikuti. Kondisi mahasiswa dan banyaknya organisasi yang ada membuat sulit dalam menentukan pilihan. Pada penelitian ini dilakukan pendekatan case-based reasoning untuk memberikan rekomendasi organisasi yang sebaiknya dipilih oleh mahasiswa. Berdasarkan pengujian terhadap 10 responden, 4 responden memilih organisasi sesuai dengan rekomendasi sistem.
SISTEM PREDIKSI PRODUKSI PADI DI SUMATERA MENGGUNAKAN REGRESI LINEAR Yudha, Ery Permana; Arif Rohmadi; Agung Teguh Setyadi
Jurnal Manajemen Informatika dan Sistem Informasi Vol. 8 No. 1 (2025): MISI Januari 2025
Publisher : LPPM STMIK Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36595/misi.v8i1.1411

Abstract

Pulau Sumatera merupakan salah satu pulau yang menjadi lumbung padi nasional karena sebagai salah satu daerah penghasil padi terbesar di Indonesia. Namun, produktivitas yang tinggi di pulau Sumatera juga terdapat beberapa tantangan seperti perubahan iklim yang tidak menentu, luas lahan, curah hujan, kelembapan, dan suhu rata-rata. Untuk mengatasi permasalahan tersebut perlu strategi yang inovatif dan berbasis data. Salah satu strategi tersebut dengan menerapkan pengolahan data untuk menghasilkan model prediksi produktivitas padi. Teknik ini melibatkan algoritma dan pembelajaran mesin untuk menganalisis pola dan tren dalam pertanian. Model ini mempermudah stakeholder terkait untuk mempersiapkan kebutuhan pangan nasional agar selalu terpenuhi. Pada penelitian ini, diusulkan sebuah metode prediksi produktivitas padi di Sumatera menggunakan metode regresi linear. Penelitian ini menghasilkan model prediksi masing-masing di setiap provinsi di Sumatera. Secara umum, tahapan yang dilakukan yaitu preprocessing, seleksi fitur, training dan testing, dan evaluasi. Uji coba yang dilakukan dengan menghitung nilai Mean Squarred Error (MSE). Beberapa algoritma yaitu Regresi Linear, Support Vector Regression (SVR), Random Forest Regression (RFR) menghasilkan nilai rata-rata MSE sebesar 0,022; 0,075; 0,026. Regresi linear mampu menghasilkan model yang lebih baik dibandingkan metode SVR dan RFR.
Comparative Analysis of Machine Learning Algorithms with RFE-CV for Student Dropout Prediction Utami, Sekar Gesti Amalia; Setiadi, Haryono; Rohmadi, Arif
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4695

Abstract

The high dropout rate of students in higher education is a problem faced by educational institutions, impacting quality assessments and accreditation evaluations by BAN-PT. This study aims to develop an early prediction model of potential dropout students using demographic data with a learning analytics approach. Five classification algorithms are used in this research, namely Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), Light Gradient Boosting Machine (LGBM), and Support Vector Machine (SVM). The dataset used consists of undergraduate student data of Sebelas Maret University in 2013 (n=2476) which is processed through preprocessing techniques, resampling with SMOTE, and validation using K-Fold Cross-Validation. The results showed that the RF model gave the best performance with an accuracy of 96.01%, followed by LGBM (95.26%), DT (91.24%), LR (83.68%), and SVM (83.19%). The use of the Recursive Feature Elimination with Cross-Validation (RFE-CV) feature selection method was able to improve the efficiency of the model by reducing the number of features without significantly degrading performance. The best feature selection was obtained when using 75% features, which provided an optimal balance between the number of features and model accuracy. The most contributing features include IPS_range (Semester GPA range), parents' income, students' regional origin, as well as several other demographic factors. This study contributes to the development of early warning systems in higher education by providing accurate predictive models and identifying key risk factors.
Peningkatan Kualitas Administrasi Pendidikan melalui Implementasi Sistem Edu Berbasis ERP di SMP IT Insan Mulia Surakarta, Jawa Tengah Widoyono, Bambang; Saptono, Ristu; Rohmadi, Arif; Syaifuddin, Akhmad; Hendra, Brilyan; Anggoro, Rizal Dwi; Ibrahim, Muhammad Syafiq
Jurnal Abdi Masyarakat Indonesia Vol 5 No 6 (2025): JAMSI - November 2025
Publisher : CV Firmos

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54082/jamsi.2130

Abstract

SMP Islam Insan Mulia Surakarta-Jawa Tengah, mengalami kendala administrasi akibat sistem manualnya, terutama dalam penerimaan siswa baru (PPDB), pencatatan pembayaran, dan pengelolaan bank soal. Kendala-kendala ini menyebabkan keterlambatan, kesalahan, dan inefisiensi, sehingga membatasi kualitas layanan. Untuk mengatasi hal ini, dalam program pengabdian masyarakat kami mengimplementasikan sistem EDU berbasis ERP sebagai solusi terintegrasi. Sistem ini menggunakan model waterfall untuk analisis, perancangan, implementasi, pelatihan, dan pengujian yang diterapkan selama tiga bulan. Tiga modul diimplementasikan: PPDB, pembayaran, dan bank soal, yang diuji coba kepada 23 peserta. Evaluasi menunjukkan hasil positif dengan efisiensi (4,08), efektivitas (4,08), dampak (4,38), kepuasan (4,28), dan kemudahan penggunaan (4,17) pada rentang skala 1-5. Program pengabdian ini tidak hanya menyelesaikan kendala administratif di SMP Islam Insan Mulia Surakarta, tetapi juga menghadirkan model implementasi sistem informasi berbasis ERP yang dapat direplikasi di sekolah lain. Digitalisasi administrasi melalui modul PPDB, pembayaran, dan bank soal terbukti meningkatkan efisiensi, transparansi, dan profesionalisme tata kelola pendidikan secara umum.
A model for determining stock purchase decisions based on gated recurrent units and decision tree C4.5 Muhammad Arsyad Rayhan Aziis; Wiranto; Arif Rohmadi
Journal of Soft Computing Exploration Vol. 7 No. 2 (2026): June 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i2.133

Abstract

The stock market is highly volatile but offers high profit potential. This makes it difficult for novice investors to make investment decisions. Current studies mostly focus on stock price prediction or trading signal classification, while interpretable hybrid frameworks to support stock purchase decisions are still limited. Many machine learning models offer limited interpretability. Numerical predictions are often generated by forecasting models, which are difficult to translate into investment decisions. Therefore, this study aims to design a hybrid decision support system with an interpretable model by combining a Gated Recurrent Unit (GRU) and a C4.5 Decision Tree classifier in stock purchase decision making. The proposed framework consists of two phases. The first step is to predict the closing price of stocks using the historical daily data with the GRU model. The predicted price is then combined with technical indicators such as Simple Moving Average (SMA), Relative Strength Index (RSI) and Moving Average Convergence Divergence (MACD) to produce trading signals using C4.5 Decision Tree. The dataset used in this study is BBNI.JK stock data from 2015 to 2025 with a walk-forward validation scheme and evaluation using RMSE, MAE, MAPE, Accuracy, Precision, Recall and F1-Score. The experimental results show that the MAPE of the GRU model is 6.16% and the proposed DT-GRU strategy produces the highest trading return of 83.07% with a Sharpe ratio of 2.75. These results indicate that the combination of interpretable forecasting and classification can provide effective and practical trading decision-making support.
Information management of critical knowledge in an IT consulting company Widoyono, Bambang; Saptono, Ristu; Rohmadi, Arif; Wihidayat, Endar; Syaifuddin, Akhmad; Hendrasuryawan, Brilyan
Jurnal Kajian Informasi dan Perpustakaan Vol 14, No 1 (2026): Accredited by Ministry of Education, Culture, Research and Technology of the Re
Publisher : Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/jkip.v14i1.67577

Abstract

Background: Knowledge Management (KM) plays a crucial role in supporting organizational sustainability, particularly in IT consulting firms where knowledge is predominantly tacit, experience-based, and vulnerable to loss. Many consulting organizations face difficulties identifying which knowledge is truly critical and how to manage it as reusable organizational information. Purpose: This study aimed to identify, prioritize, and manage critical knowledge aligned with organizational strategy at PT XYZ, an IT consulting company. Methods: Using a mixed qualitative–quantitative approach, this study followed six stages: contextual knowledge scoping, tacit knowledge elicitation, knowledge structuring, critical knowledge assessment using Critical Knowledge Factors (CKF), prioritization using Analytical Hierarchy Process (AHP), and repository design for knowledge preservation. Results: The study identified 24 structured knowledge areas, of which 20 were classified as critical. AHP analysis indicated that Gaining Commitment, Reading Opportunities, and Marketing Strategy were the highest priority knowledge assets, primarily embedded in sales and marketing activities. Conclusion: This study demonstrates how tacit knowledge can be transformed into structured organizational information aligned with strategic processes through an information management perspective. Theoretically, this study contributes to information science by conceptualizing critical knowledge as information objects organized through metadata, lifecycle governance, and repository preservation. Implications: In practice, these findings provide the IT consulting firm with a structured approach to safeguarding critical tacit knowledge, reducing reliance on individuals, and strengthening organizational memory. However, the study is limited to a single organizational context, relies on expert judgment, and presents a repository design that remains conceptual rather than technically implemented.
Optimization of User-Based Collaborative Filtering Movie Recommendation System Using Mean-Centering and Overlap Weighting on Cosine Similarity Arif Rohmadi; Ery Permana Yudha; Bambang Widoyono
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3315

Abstract

Rapid technological advancements have changed the way people enjoy entertainment. Viewers can now watch movies online without having to go to the cinema. The rapid development of online movie streaming services has increased the need for systems capable of providing content recommendations based on user preferences. This study aims to optimize a User-Based Collaborative Filtering (UBCF)-based recommendation system using the MovieLens 100K dataset containing 100,000 ratings from 943 users for 1,682 movies. The evaluation was conducted by dividing the data into 80% training data and 20% testing data. The system was optimized through two main approaches: user average normalization (mean-centering) in the KNN method to reduce bias in rating scale differences between users, and the application of overlap weighting on cosine similarity to give greater weight to user pairs with a greater number of shared item ratings. Based on experiments, a value of k = 50 was chosen as the optimal trade-off point in predicting ratings. The experimental results show that KNN with mean centering (KNNWithMeans) consistently outperforms standard KNN. The cosine + KNNWithMeans model produced an RMSE of 0.9701 and an MAE of 0.7567, lower than cosine + KNN (RMSE 1.0377; MAE 0.8226). Further overlap weighting was shown to improve prediction accuracy, with the combination of weighted cosine with α = 1 and KNNWithMeans providing the best performance with an RMSE of 0.9686 and an MAE of 0.7556.
Optimizing E-commerce Personalization through Hybrid Decision Tree–Nearest Neighbor Recommendation Integration Syaifuddin, Akhmad; Saptono, Ristu; Rohmadi, Arif; Widoyono, Bambang; Hendrasuryawan, Brilyan
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5418

Abstract

Single-method recommendation systems face critical limitations: content-based filtering suffers from overspecialization while collaborative filtering struggles with data sparsity and cold-start problems. This research introduces an innovative hybrid recommendation framework that synthesizes Content-Based Filtering (CBF) utilizing Decision Trees with Collaborative Filtering (CF) employing Nearest Neighbor algorithms. Our approach addresses the inherent limitations of singular recommendation methodologies by integrating product attribute analysis with collective user behavior patterns. We conducted comprehensive evaluations using a shopping behavior dataset comprising 3,900 consumer records with diverse demographic and product interaction data. Our findings reveal that an asymmetric hybrid configuration—weighted at 70% for CBF and 30% for CF—achieves optimal performance with a Root Mean Square Error (RMSE) of 0.7422. The system incorporates an interactive user interface that facilitates a natural shopping experience: browsing available items, receiving personalized recommendations, and providing explicit feedback on suggested products. Through feature importance analysis, we identified key product attributes that significantly influence recommendation quality, including size variations and specific color preferences. The hybrid approach demonstrates 42% greater category diversity and 37% more recommendation diversity compared to pure content-based filtering, while maintaining superior accuracy metrics. Our research contributes to understanding optimal hybrid architectures and provides practical insights for implementing effective personalization strategies in real-world e-commerce environments.
Co-Authors Agung Teguh Setyadi Anggoro, Rizal Dwi Bambang Widoyono Ery Permana Yudha Hendra, Brilyan Hendrasuryawan, Brilyan Ibrahim, Muhammad Syafiq Muhammad Arsyad Rayhan Aziis Ristu Saptono Setiadi, Haryono Syaifuddin, Akhmad Utami, Sekar Gesti Amalia Widoyono, Bambang Wihidayat, Endar Wiranto Yudha, Ery Permana