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All Journal Informatics for Educators and Professional : Journal of Informatics JITK (Jurnal Ilmu Pengetahuan dan Komputer) METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Indonesian Journal of Applied Informatics Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Sisfotek Global Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Sistem Informasi dan Teknologi (SINTEK) TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi AMMA : Jurnal Pengabdian Masyarakat
Willy Prihartono
STMIK IKMI Cirebon
Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing Other

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Model Machine Learning Untuk Prediksi Risiko Penyakit Liver Dengan Random Forest Teroptimasi Rizky Andrea Arifa; Nana Suarna; Agus Bahtiar; Nining Rahaningsih; Willy Prihartono
Jurnal Sistem Informasi dan Teknologi Vol 6 No 1 (2026): Jurnal Sistem Informasi dan Teknologi (SINTEK)
Publisher : LPPM STMIK KUWERA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56995/sintek.v6i1.204

Abstract

Penyakit liver merupakan salah satu kondisi kronis dengan tingkat mortalitas tinggi, sehingga diperlukan pendekatan prediksi yang akurat untuk mendukung deteksi dini. Penelitian ini bertujuan mengembangkan model machine learning untuk memprediksi risiko penyakit liver menggunakan algoritma Random Forest yang dioptimalkan dengan RandomizedSearchCV. Dataset yang digunakan terdiri dari 1.700 entri yang mencakup variabel klinis dan gaya hidup, termasuk usia, jenis kelamin, BMI, konsumsi alkohol, kebiasaan merokok, riwayat genetik, aktivitas fisik, diabetes, hipertensi, serta hasil Liver Function Test. Proses penelitian meliputi preprocessing, normalisasi skala, pembagian data menggunakan train-test split 80:20, pembangunan model baseline, dan optimasi hiperparameter. Hasil eksperimen menunjukkan bahwa optimasi menghasilkan peningkatan performa model, dengan akurasi 0.91, peningkatan recall sebesar 3.20%, dan AUC-ROC mencapai 0.96. Analisis feature importance menunjukkan bahwa LiverFunctionTest, BMI, dan AlcoholConsumption merupakan fitur paling berpengaruh terhadap prediksi risiko penyakit liver. Dengan demikian, Random Forest teroptimasi terbukti efektif dalam menghasilkan model prediksi yang akurat dan dapat digunakan sebagai alat pendukung keputusan dalam deteksi dini penyakit liver.
ALGORITMA RANDOM FOREST UNTUK PREDIKSI STATUS PINJAMAN BERDASARKAN SKOR KREDIT Hadit Attaufiqqurrohman; Ade Irma Purnamasari; Denni Pratama; Nining Rahaningsih; Willy Prihartono
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 12 No. 1 (2026): Volume 12 Nomor 1 Tahun 2026
Publisher : Universitas Methodist Indonesia

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Abstract

The rapid development of financial technology has encouraged financial institutions to adopt data-driven credit scoring systems in order to minimize the risk of default. However, many loan eligibility prediction models still face challenges such as data imbalance (class imbalance) and the limited capability of traditional models to capture non-linear relationships among variables. This study aims to develop a loan status prediction model using the Random Forest algorithm combined with the Synthetic Minority Oversampling Technique (SMOTE) and One-Hot Encoding (OHE) to improve model accuracy and generalization capability. The data used in this study are secondary data obtained from the public Kaggle platform, consisting of 45,000 records with 14 demographic and financial attributes. The research method employs a supervised learning approach with several stages, including data acquisition and preprocessing (data cleaning, normalization, encoding, and data balancing), Random Forest model training, and performance evaluation using accuracy, precision, recall, F1-score, and AUC metrics. The results show that the combination of Random Forest, SMOTE, and OHE achieves high predictive performance, with an accuracy of 94.8%, precision of 95.6%, recall of 93.7%, F1-score of 94.6%, and an AUC value of 0.972. The most influential variables in loan status prediction are credit_score, person_income, and loan_amnt. This approach is proven to be effective in addressing data imbalance issues and improving classification accuracy in identifying creditworthy and non-creditworthy borrowers.
Penerapan Algoritma C4.5 untuk Optimalisasi Manajemen Stok Obat di Apotek Nafa Farma Khairunnisa Amarullah; Rini Astuti; Willy Prihartono; Ryan Hamonangan
IJAI (Indonesian Journal of Applied Informatics) Vol 9, No 2 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v9i2.96224

Abstract

Abstrak : Manajemen stok obat menjadi tantangan utama di apotek Nafa Farma untuk mencegah kelebihan atau kekurangan stok. Penelitian ini mengaplikasikan algoritma C4.5 sebagai metode klasifikasi untuk mendukung pengelolaan stok yang optimal. Data stok obat dari Januari sampai Desember 2023 dianalsis menggunakan pendekatan Knowledge Discovery in Database (KDD) dengan software RapidMiner. Penelitian ini menunjukkan bahwa algoritma C4.5 dapat meningkatkan efisiensi manajemen stok obat dengan akurasi 80,67% dan F1-score rata-rata 80.52% ini memberikan rekomendasi strategis untuk pengadaan obat. Obat kategori laku direkomendasikan untuk diutamakan dalam pengadaan, sementara obat tidak laku dapat dikurangi pembeliannya untuk menghindari pemborosan. Algoritma C4.5 efektif untuk meningkatkan efisiensi pengelolaan stok obat====================================================Abstract : Drug stock management is a major challenge at Nafa Farma pharmacy to prevent excess or shortage of stock. This research applies the c4.5 algorithm as a classification method to support optimal stock management. Drug stock data from January to December 2023 was analyzed using a Knowledge Discovery in Database (KDD) approach with RapidMiner software. This study shows that the C4.5 algorithm can improve the efficiency of drug stock management with an accuracy of 80.67% and an average F1-score of 80.52%, providing strategic recommendations for drug procurement.  Sellable category drugs are recommended to be prioritized in procurement, while unsellable drugs can be reduced in purchase to avoid waste. The C4.5 algorithm is effective in improving the efficiency of drug stock management.
Comparison of Balancing Strategies for Classifying Guava Fruit Diseases Putri Nabilla; Nana Suarna; Agus Bahtiar; Nining Rahaningsih; Willy Prihartono
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1859

Abstract

The problem of class imbalance often poses an obstacle in deep learning-based image classification, especially in the domain of digital agriculture. The imbalance in data distribution makes it easier for models to recognize the majority class, while performance for the minority class declines. This study aims to analyze the effectiveness of three strategies for handling class imbalance: Weighted Loss Function, Oversampling, and a combination of Weighted Loss and Oversampling, in improving the performance of image classification of guava fruit diseases using a transfer learning-based MobileNetV2 architecture. The dataset consists of 3,784 images of three disease classes, namely Anthracnose, Fruit_Fly, and Healthy_guava, which show an imbalanced distribution. The research was conducted through the stages of Exploratory Data Analysis (EDA), pre-processing, augmentation, model training with four scenarios, and evaluation using Accuracy, Precision, Recall, F1-Score, and Macro Average F1-Score. The results showed that the Combination model (Oversampling and Weighted Loss) performed best on the minority class with an F1-score of 0.9630, the highest among all models. The Oversampling strategy produced the highest Macro F1-score of 0.9617, while Weighted Loss provided a significant improvement in classification sensitivity but was still below the combination model. Thus, it can be concluded that the combination strategy is the most effective approach in improving the sensitivity of the model to minority classes, while Oversampling excels in the overall performance stability of the model.
Model Pendampingan Transformasi Digital Terintegrasi Dalam Meningkatkan Kemandirian Dan Daya Saing UMKM Kota Cirebon Bani Nurhakim; Willy Prihartono; Putri Indah Lestari; Revan Faturochman
AMMA : Jurnal Pengabdian Masyarakat Vol. 4 No. 5 : Juni (2025): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

Digital transformation has become a strategic necessity for Micro, Small, and Medium Enterprises (MSMEs) as they navigate shifting consumer behaviors, intensifying competition, and the evolution of technology-driven trade ecosystems. However, many MSMEs still face various obstacles, including limited digital literacy, suboptimal use of social media and online marketplaces, weak brand identity, disorganized administration and financial record-keeping, and a lack of integration between marketing, transaction, and customer management processes. This community service initiative aims to implement an integrated digital transformation mentoring model to enhance the self-reliance and competitiveness of MSMEs in Cirebon City. The implementation employs a participatory and needs-based mentoring approach, comprising stages such as needs identification, digital maturity mapping, training, technology implementation, intensive mentoring, monitoring, and evaluation. The mentoring program focuses on five key dimensions: digital branding, digital marketing, digital commerce, digital payments, and digital business management. Results indicate that this phased and integrated mentoring approach successfully improved participants' understanding of digital transformation, their ability to manage business social media, the quality of promotional content, the utilization of digital sales channels and payment methods, and their awareness regarding the importance of business data recording. This model underscores that MSME digital transformation cannot be achieved solely through one-way training; rather, it requires a continuous process involving assessment, hands-on practice, mentoring, evaluation, and follow-up. The program is expected to serve as a replicable and scalable MSME empowerment model, fostered through collaboration among higher education institutions, local government, business communities, and other stakeholders.
Implementasi Algoritma Naïve Bayes untuk Prediksi Penerima Bantuan Sosial di Desa Cigayam Dede Hoeriah; Bani Nurhakim; Sandy Eka Permana; Willy Prihartono; Gifthera Dwilestari
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 4 No 1 (2024): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol4No1.pp52-58

Abstract

Social assistance is one of the government's programmes aimed at improving the lives of people especially for those who are economically disadvantaged. However, there are several reasons why some people are unable to access social assistance. In the case of this study, the authors used the Naïve Bayes algorithm with the KDD (Knowledge Discovery Database) method to predict the population in obtaining social assistance. The data was taken from the population data of Cigayam Village and the social welfare recipient data in the village ofCigayam with the results showing high accuracy in this study, for the true or false outcome of 1047 data and 53 data with the precision grade of 95.18%, 81.17%, for the real outcome, and 28.38% for the wrong outcome. So with the ROC curve shows the accuracy of the spinning visually, with an AUC of 0.868% for naïve bayes using the ROK curve of 0.90.1.
Co-Authors Ade Irma Purnamasari Ade Rizki Rinaldi Agung Saeful Agung Triyono Aliya Anisa Rahma Andre Setiawan Arief Ilham Syahputra Arya Hadi Wicaksana Astri Amelia Bani Nurhakim Cep Lukman Rohmat Dea Eryanti Putri Dede Hoeriah Denni Pratama Gifthera Dwilestari Hadit Attaufiqqurrohman Irma Agustina Khairunnisa Amarullah Martanto . Nana Suarna Nining Rahaningsih Nur Kholidin Nursyifa Puspa Ar-rahmi Slamet Odi Nurdiawan Opiana . Putri Indah Lestari Putri Nabilla Raditya Danar Dana Revan Faturochman Rini Astuti Rizky Andrea Arifa Ryan Hamonangan Sandy Eka Permana Sri Farida Utami Umi Hayati Yudhistira Arie Wijaya