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All Journal Jurnal Ilmiah Informatika Komputer Jurnal Terapan Abdimas Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial IJCIT (Indonesian Journal on Computer and Information Technology) JURNAL TEKNOLOGI DAN OPEN SOURCE Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Scientific Journal of Informatics Infotek : Jurnal Informatika dan Teknologi Jurnal Teknologi Informatika dan Komputer SKANIKA: Sistem Komputer dan Teknik Informatika Akrab Juara : Jurnal Ilmu-ilmu Sosial Computer Science (CO-SCIENCE) Reputasi: Jurnal Rekayasa Perangkat Lunak Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Aruna Mengabdi (ARMI) Jurnal Kecerdasan Buatan dan Teknologi Informasi Jurnal Sistem Komputer (SISKOM) Indonesian Community Service Journal of Computer Science (IndoComs)
Ahmad Fauzi
Universitas Bina Sarana Informatika
Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Medicine & Pharmacology Public Health Social Sciences Other

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Penerapan Platform Perangkat Lunak Bisnis Terpadu Aplikasi Oddo Pada Mitra Kelompok Tani Lele Minda Septiani; Ahmad Fauzi; Verra Sofica; Nila Hardi
Indonesian Community Service Journal of Computer Science Vol. 3 No. 1 (2026): Periode Januari 2026
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/indocoms.v3i1.11904

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan tata kelola administrasi dan keuangan Kelompok Tani Lele BP. Sumberjaya melalui penerapan platform perangkat lunak bisnis terpadu Odoo. Kelompok tani yang bergerak di bidang budidaya lele ini memerlukan sistem pencatatan yang terstruktur untuk mengelola pengeluaran operasional dan transaksi penjualan hasil panen. Metode pelaksanaan meliputi analisis kebutuhan mitra, instalasi dan konfigurasi aplikasi, pelatihan, serta pendampingan penggunaan modul Expense dan Invoice. Hasil kegiatan menunjukkan bahwa penerapan Odoo mampu meningkatkan akurasi dan transparansi pencatatan keuangan, meminimalisir kesalahan manual, serta mempermudah penyusunan laporan keuangan. Selain itu, mitra memperoleh pemahaman yang lebih baik mengenai pengelolaan administrasi berbasis digital sehingga mendukung pengambilan keputusan usaha dan peningkatan profesionalisme kelompok tani.
KLASIFIKASI CITRA SAYUR-SAYURAN MENGGUNAKAN CNN DENGAN VALIDASI SILANG K-FOLDS DI TENSORFLOW Ahmad Fauzi; Septian Rizky Insani Rizky Insani; Bambang Wijonarko
Reputasi: Jurnal Rekayasa Perangkat Lunak Vol. 6 No. 2 (2025): Edisi November 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/reputasi.v6i2.9258

Abstract

Sayuran merupakan bagian tumbuhan yang memiliki kadar air tinggi dan berperan penting dalam konsumsi pangan sehari-hari. Namun, proses identifikasi jenis sayuran secara manual sering kali memerlukan waktu dan berisiko terjadi kesalahan, terutama dalam skala besar seperti di pasar atau industri. Penelitian ini bertujuan untuk mengembangkan dan menguji model klasifikasi citra sayuran secara otomatis menggunakan algoritma Convolutional Neural Network (CNN) dengan pendekatan K-Fold Cross Validation sebagai solusi terhadap permasalahan tersebut. Dataset yang digunakan terdiri dari 15 kelas sayuran, yaitu brokoli, capsicum, kacang, kembang kol, kentang, kubis, labu air, labu kuning, lobak, pare, pepaya, terong, timun, tomat, dan wortel. Proses pelatihan dilakukan dengan dua skenario, yaitu model pertama dilatih selama 10 epoch dan model kedua selama 5 epoch, masing-masing divalidasi menggunakan 5-Fold Cross Validation. Hasil pengujian menunjukkan bahwa model dengan pelatihan 10 epoch memberikan performa klasifikasi yang lebih optimal dengan akurasi tertinggi sebesar 99,13%, dibandingkan dengan model 5 epoch yang mencapai akurasi 99,07%. Temuan ini menegaskan bahwa jumlah epoch berpengaruh signifikan terhadap peningkatan performa model CNN dalam tugas klasifikasi citra dengan banyak kelas. Model yang dihasilkan memiliki potensi besar untuk diterapkan dalam sistem identifikasi otomatis sayuran guna mendukung sektor pertanian dan perdagangan berbasis teknologi.
XGBOOST-BASED FRAUD TRANSACTION CLASSIFICATION ANALYSIS IN ONLINE PAYMENT SYSTEMS Sri Diantika; Hiya Nalatissifa; Riki Supriyadi; Nurlaelatul Maulidah; Ahmad Fauzi
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 2 (2026): May 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i2.478

Abstract

The rapid development of online payment systems has significantly facilitated digital transactions; however, it has simultaneously increased the risk of fraudulent activities. Fraud detection has become a critical challenge due to the complex characteristics of transaction data and the imbalanced class distribution between legitimate and fraudulent transactions. This study aims to analyze the performance of the XGBoost algorithm in classifying fraudulent transactions within online payment systems. The research employs the Online Payments Fraud Detection Dataset obtained from the Kaggle platform. The research methodology consists of several stages, including dataset collection, data preprocessing, categorical data transformation using label encoding, feature engineering for the generation of new attributes, data partitioning through split validation with an 80:20 ratio, model development using the XGBoost algorithm, and performance evaluation using a confusion matrix, accuracy, precision, recall, F1-score, and Area Under the Curve (AUC). The experimental results demonstrate that the XGBoost model achieves excellent classification performance, with an accuracy of 99.98%, precision of 85%, recall of 100%, F1-score of 92%, and an AUC value of 0.9996. Furthermore, feature importance analysis reveals that errorOrig and newbalanceOrig are the most influential attributes in detecting fraudulent transactions. Based on these findings, it can be concluded that the XGBoost algorithm is highly effective for fraud transaction classification in online payment systems and exhibits strong potential for implementation in automated fraud detection systems to enhance the security of digital financial transactions.
Measuring the Quality of the General Election Commission Website in Central Jakarta Using the WebQual 4.0 Method Naufal Brilianto; Ahmad Fauzi; Artika Surniandari; Hilda Rachmi
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 7 No. 2 (2024): Jurnal Teknologi dan Open Source, December 2024
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v7i2.3876

Abstract

This study aims to analyze the application of the Webqual 4.0 method in measuring the quality of the Central Jakarta City General Election Commission (in Indonesia it is abbreviated as KPU) website. This method focuses on three dimensions of usability quality, information quality, and interaction quality. This study uses a quantitative method with primary data. The sample of this study were employees and the general public. The data analysis technique used was multiple linear analysis processed using the SPSS version 26 program. The results of the study showed that the usability variable did not have a significant effect on website quality measurement. The Information Quality variable did not have a significant effect on website quality measurement. And the Interaction Quality variable had a significant effect on website quality measurement.
ANALISIS KANKER PARU-PARU MENGGUNAKAN ALGORITMA LOGISTIC REGESSION DAN RANDOM FOREST Zulia Imami Alfianti; Ginabila Ginabila; Ahmad Fauzi; Risca Lusiana Pratiwi
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 8 No 1 (2026): EDISI 27
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v8i1.7063

Abstract

Kanker paru-paru merupakan salah satu jenis kanker dengan tingkat kematian tertinggi di dunia, yang disebabkan oleh faktor gaya hidup seperti merokok dan konsumsi alkohol, serta faktor genetik. Mengingat deteksi dini konvensional memerlukan waktu dan biaya besar, penelitian ini mengusulkan pendekatan Machine Learning yang lebih efisien untuk memprediksi risiko penyakit. Menggunakan algoritma Logistic Regression dan Random Forest pada dataset Survey Lung Cancer yang berisi 309 responden dengan 16 variabel gaya hidup dan kesehatan , penelitian ini melibatkan tahapan data understanding, data preparation (termasuk encoding dan scaling), modeling, dan evaluation. Hasil analisis menunjukkan performa yang sangat baik untuk kedua algoritma dengan nilai Akurasi 96,77% dan nilai Presisi, Recall, serta F1-score mencapai 0,9833. Meskipun metrik utama identik, perbandingan kurva ROC menunjukkan bahwa model Random Forest (AUC = 0,958) sedikit lebih unggul dari Logistic Regression (AUC = 0,917). Berdasarkan analisis, faktor usia (AGE) teridentifikasi sebagai variabel paling berpengaruh terhadap risiko kanker paru-paru, diikuti oleh konsumsi alkohol, alergi, dan tekanan sosial7. Hasil ini diharapkan menjadi referensi dalam pengembangan sistem prediksi dan deteksi dini berbasis Machine Learning.
Penerapan Algoritma Naive Bayes dan SVM untuk Analisis Sentimen terhadap Penggunaan True Wireless Stereo (TWS) Risca Lusiana Pratiwi; Zulia Imami Alfianti; Ahmad Fauzi; Ginabila Ginabila
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 8 No 2 (2025): Jurnal SKANIKA Juli 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v8i2.3535

Abstract

The use of wireless audio devices such as True Wireless Stereo (TWS) has become increasingly popular among Indonesian society as a solution to the limitations of wired earphones. As TWS usage continues to grow, understanding public sentiment toward these devices becomes essential to support product development and assist consumers in making informed purchasing decisions. This study aims to analyze user sentiment toward TWS on the social media platform X using the Naive Bayes and Support Vector Machine (SVM) algorithms. To improve classification performance, the Synthetic Minority Oversampling Technique (SMOTE) is applied to handle imbalanced data, while Particle Swarm Optimization (PSO) is used to optimize the model. The results show that the SVM algorithm outperforms Naive Bayes, achieving an accuracy of 80.46% and an AUC score of 0.854, with more balanced precision and recall values across both classes. Meanwhile, Naive Bayes demonstrated strength in detecting negative sentiment but with a lower accuracy of 78.00% and an AUC of 0.780
Analisis Kualitas Layanan Terhadap Kepuasan Pengguna Aplikasi Clean Hris Menggunakan Metode Webqual 4.0 Maria Fatima Jedo Tukan; Mochammad Abdul Azis; Ahmad Fauzi; Ginabila
Jurnal Sistem Komputer (SISKOM) Vol. 6 No. 2 (2026): Mei
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/siskom.v6i2.1871

Abstract

This study aims to analyze the effect of Clean HRIS website service quality on user satisfaction using the WebQual 4.0 method. The independent variables in this study consist of usability, information quality, and service interaction quality, while the dependent variable is user satisfaction. This research applies a quantitative approach, with data collected through questionnaires distributed to 100 active users of Clean HRIS. The data were analyzed using validity testing, reliability testing, classical assumption testing, multiple linear regression, t-test, F-test, and coefficient of determination analysis. The results show that the three WebQual 4.0 variables, namely usability, information quality, and service interaction quality, have a positive and significant effect on user satisfaction. The coefficient of determination indicates that service quality variables are able to explain a substantial proportion of the variation in user satisfaction with the Clean HRIS website. Descriptively, users are categorized as satisfied with the aspects of usability, information quality, and service interaction quality. Therefore, the service quality of the Clean HRIS website plays an important role in improving user satisfaction. This study recommends that application managers continuously improve information clarity, ease of navigation, interface design, and service responsiveness to enhance user satisfaction in a sustainable manner.
APPLICATION OF CONVOLUTIONAL NEURAL NETWORKS FOR THE CLASSIFICATION OF ORGANIC AND INORGANIC WASTE IMAGES Syifa Salsabila; Ahmad Fauzi
Akrab Juara : Jurnal Ilmu-ilmu Sosial Vol. 11 No. 2 (2026): Mei
Publisher : Yayasan Azam Kemajuan Rantau Anak Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Waste management has become one of the most pressing environmental challenges due to the increasing volume of waste and the limited public awareness of proper waste segregation. The inability to distinguish between organic and inorganic waste often reduces the effectiveness of recycling and sustainable waste management practices. This study aims to develop an automatic image classification system based on the Convolutional Neural Network (CNN) method to identify organic and inorganic waste from digital images. The dataset consists of 25,077 images collected from publicly available sources and self-collected data, which were divided into 90% training data and 10% testing data. Before model training, the images underwent preprocessing techniques, including resizing, normalization, and data augmentation, to improve model performance and generalization. The CNN model was developed using the TensorFlow framework and achieved a training accuracy of 93.08% at the ninth epoch. Furthermore, the trained model was integrated into a Flask-based web application, enabling users to upload waste images and instantly receive classification results. The findings demonstrate that the proposed CNN-based system effectively classifies organic and inorganic waste images and has the potential to support intelligent waste management systems by improving the efficiency and accuracy of waste sorting. This research also highlights the applicability of deep learning techniques in addressing environmental issues through automated image recognition technology.
TRANSFORMASI DIGITAL DENGAN IMPLEMENTASI APLIKASI WEB PADA YAYASAN FADILLAH ILMI PRATAMA DEPOK Sunarti; Dewi Ayu Nur Wulandari; Ahmad Fauzi; Hana Dwi Lestari; Anjelika Maulida
Jurnal Terapan Abdimas Vol. 10 No. 1 (2025)
Publisher : UNIVERSITAS PGRI MADIUN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25273/jta.v10i1.21181

Abstract

Abstract. Digital transformation through implementing the website https://fadhillahilmi.com can increase the efficiency and effectiveness of data management and educational services at the Fadhillah Ilmi Pratama Depok Foundation. This community service explores the application of information technology in supporting foundation activities, especially in accepting new students. By developing a web-based information system, the foundation can speed up access to information for the public, student registration, and digital payment recording. The website can solve partner problems in the field of service, partner knowledge, and skills. The activity aims to carry out digital transformation to support the activities and management of new students at the Fadhillah Ilmi Pratama Foundation in providing services and wider information to improve the quality of education. The method used is learning by doing, starting with a direct inspection of the field, monitoring and evaluating the results of the training that has been carried out, with implementation stages including analysis of situations and conditions, preparation for appropriate technology transfer, training, simulation and mentoring followed by monitoring and evaluation activities. The results showed significant improvements in information accessibility and user satisfaction. The training provided succeeded in increasing partners' knowledge and skills in utilizing information technology with partners' understanding of the partner's level of knowledge and understanding of the website for the pre-test at 30% and 94%.   Abstrak. Transformasi digital melalui implementasi website https://fadhillahilmi.com dapat meningkatkan efisiensi dan efektivitas pengelolaan data serta pelayanan pendidikan pada yayasan Fadhillah Ilmi Pratama Depok. Pengabdian kepada masyarakat ini mengeksplorasi penerapan teknologi informasi dalam mendukung aktivitas yayasan, khususnya dalam proses penerimaan siswa baru. Dengan mengembangkan sistem informasi berbasis web, yayasan dapat mempercepat akses informasi bagi masyarakat, pendaftaran siswa, dan pencatatan pembayaran secara digital. Website dapat menyelesaikan permasalahan mitra bidang pelayanan, pengetahuan dan keterampilan mitra. Kegiatan bertujuan melakukan transformasi digital mendukung aktivitas dan pengelolaan siswa baru pada yayasan Fadhillah Ilmi Pratama dalam melakukan pelayanan dan penyampaian informasi yang lebih luas untuk meningkatkan kualitas pendidikan. Metode yang digunakan learning by doing diawali peninjauan langsung kelapangan, monitoring dan evaluasi dari hasil pelatihan yang sudah terlaksana, dengan tahapan pelaksanaan meliputi: analisa situasi dan kondisi, persiapan transfer teknologi tepat guna, pelatihan, simulasi dan pendampingan dilanjutkan kegiatan monitoring dan evaluasi. Hasil penelitian menunjukkan peningkatan signifikan dalam aksesibilitas informasi dan kepuasan pengguna. Pelatihan yang diberikan berhasil meningkatkan pengetahuan dan keterampilan mitra dalam memanfaatkan teknologi informasi dengan pemahaman mitra untuk tingkat pengetahuan dan pemahaman mitra tentang website untuk pre-test sebesar 30% dan post-test sebesar 94%.
Co-Authors Andika Bayu Hasta Yanto Anjelika Maulida Artika Surniandari Aziz Setyawan Hidayat, Aziz Setyawan Bambang Wijonarko Dewi Ayu Nur Wulandari Diantika, Sri Erniawati Erniawati Febri Ainun Jariyah Ginabila Hamdun Sulaiman Hana Dwi Lestari Hilda Rachmi Hiya Nalatissifa Imam Nawawi Maria Fatima Jedo Tukan Michelyn Angela Sabatini Rajagukguk Mochammad Abdul Azis Naufal Brilianto Nila Hardi Novita Indriyani Nurlaelatul Maulidah Pas Mahyu Akhirianto Riki Supriyadi Risa Wati - AMIK BSI Tasikmalaya Risa Wati - AMIK BSI Tasikmalaya Risca Lusiana Pratiwi Robi Aziz Zuama Safitri Linawati Septian Rizky Insani Rizky Insani Silvy Nur Azizah Sunarti Syifa Salsabila Taufik Hidayatulloh Verra Sofica Wandy Ubleeuw Zulia Imami Alfianti