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All Journal Jurnal Informatika SISFOTENIKA Indonesian Journal on Software Engineering (IJSE) Jurnal Teknik Komputer AMIK BSI Jurnal Pilar Nusa Mandiri Techno Nusa Mandiri : Journal of Computing and Information Technology Jurnal Teknologi Sistem Informasi dan Aplikasi INTI Nusa Mandiri JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Infortech Jurnal Teknimedia: Teknologi Informasi dan Multimedia Jurnal Sistem Informasi Computer Science (CO-SCIENCE) SPEED - Sentra Penelitian Engineering dan Edukasi Jurnal Teknologi Sistem Informasi J-Innovation Indonesian Journal Computer Science (ijcs) Journal Medical Informatics Technology Jurnal Pengabdian Kepada Masyarakat International Journal of Computer Technology and Science
Taopik Hidayat
Universitas Nusa Mandiri
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Medicine & Pharmacology Public Health Social Sciences Other

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Prediksi Kelulusan Mahasiswa Menggunakan Algoritma C4.5 dengan RapidMiner: Studi Kasus Data Akademik Perguruan Tinggi XYZ Khasanah, Nurul; Saputri, Daniati Uki Eka; Hidayat, Taopik; Aziz, Faruq
Indonesian Journal Computer Science Vol. 4 No. 2 (2025): Oktober 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ijcs.v4i2.9647

Abstract

Ketepatan waktu kelulusan mahasiswa merupakan indikator penting dalam menilai kualitas dan efektivitas pendidikan tinggi. Keterlambatan kelulusan tidak hanya berdampak pada akreditasi program studi, tetapi juga pada efisiensi manajemen akademik dan kesiapan lulusan menghadapi dunia kerja. Penelitian ini bertujuan membangun model prediksi kelulusan mahasiswa menggunakan algoritma C4.5 berbasis pohon keputusan dengan dukungan perangkat lunak RapidMiner. Dataset yang digunakan terdiri atas 379 entri mahasiswa, yang mencakup atribut demografis (jenis kelamin, umur, status nikah), status mahasiswa, indeks prestasi semester (IPS 1–8), serta indeks prestasi kumulatif (IPK). Proses penelitian meliputi pengumpulan, pembersihan, transformasi data, pemodelan, dan evaluasi performa. Model diuji menggunakan pembagian data 70:30 serta validasi silang (10-fold cross-validation) untuk memastikan keandalan hasil. Hasil pengujian menunjukkan akurasi 97,81% dan nilai AUC 0,991, yang menegaskan kemampuan algoritma C4.5 dalam mengklasifikasikan status kelulusan secara tepat. Temuan ini menunjukkan peningkatan signifikan dibandingkan penelitian sebelumnya dengan algoritma Naïve Bayes (88,16%) dan K-NN (87,8%). Atribut yang paling berpengaruh adalah IPS3, IPS4, IPS5, status pekerjaan, dan umur mahasiswa. Penelitian ini berkontribusi pada pengembangan model prediksi yang tidak hanya akurat, tetapi juga mudah diinterpretasikan, sehingga dapat dimanfaatkan oleh institusi pendidikan dalam menyusun kebijakan intervensi akademik dini bagi mahasiswa berisiko terlambat lulus.
Analisis Komparatif Metode Peningkatan Kualitas Citra Digital untuk Deteksi Area Tubercoluma pada Citra MRI Hidayat, Taopik; Dama, Desi Masdin; Irmanti, Kanita Salsabila Dwi
J-Innovation Vol. 13 No. 2 (2024): Jurnal J-Innovation
Publisher : Politeknik Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55600/jipa.v13i2.289

Abstract

Digital images are essential visual representations in medical analysis, particularly for detecting tuberculoma, a severe tuberculosis complication with high mortality. This study aims to enhance the quality of MRI T1 images for identifying specific tuberculoma areas by comparing four method segmentation, thresholding, negation, and embossed. The dataset comprises T1 MRI brain scans in top and bottom positions, followed by pre-processing stages to reduce noise using gaussian blurring, median blurring, and sharpening techniques. The pre-processing results indicate all methods retain image details effectively. Further analysis reveals the Embossed method produces the clearest images with high contrast, facilitating tuberculoma identification. The advantage of this method lies in its ability to highlight structural details through a three-dimensional effect. The findings conclude that the embossed method effectively improves the accuracy of tuberculoma detection, contributing significantly to advancing medical image analysis techniques. This research is expected to positively impact imaging-based disease diagnosis, particularly in brain tumor cases like tuberculoma.
Optimalisasi Teknik Reduksi Noise: Studi Perbandingan Metode Filtering untuk Peningkatan Citra Hidayat, Taopik; Ihsan Aulia Rahman; Rianggi Silvi Anti Butar-Butar
J-Innovation Vol. 14 No. 2 (2025): Jurnal J Innovation
Publisher : Politeknik Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55600/jipa.v14i2.315

Abstract

Digital image restoration is a critical aspect of image processing, as noise introduced during acquisition, transmission, or compression can degrade visual quality and reduce the accuracy of image information. The main challenge in noise reduction lies in suppressing disturbances without damaging important image details and structural features. This study aims to evaluate the effectiveness of Gaussian Filter, Median Filter, and Mean Filter, both individually and in combination, for noise reduction in digital images. The dataset consists of JPG images with a resolution of 4032×3024 pixels (12 MP), acquired using a smartphone camera and artificially contaminated with noise to simulate real-world conditions. Performance evaluation was conducted using noise standard deviation, Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM). Experimental results indicate that the combination of the Median Filter and Gaussian Filter achieves the best overall performance, with a noise standard deviation of 88.08, a PSNR of 13.32 dB, and an SSIM of 0.15, demonstrating an optimal balance between noise reduction and structural preservation. The findings confirm that combined filtering approaches are more effective than single filters. Future research is recommended to explore advanced filtering methods such as Bilateral Filter, Wiener Filter, and adaptive filtering techniques under various noise conditions
Pelatihan AI untuk Optimalisasi Kegiatan Yayasan IRMA Menuju Era Transformasi Digital Taopik Hidayat; Syarah Seimahuira; Andi Saryoko; Retno Sari; Bagas Eka Saputra; Naufal Muzakki Ramadhan; Satrio Budi Santoso
Jurnal Pengabdian kepada Masyarakat Vol. 12 No. 1 (2025): JURNAL PENGABDIAN KEPADA MASYARAKAT 2025
Publisher : P3M Politeknik Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/abdimas.v12i1.6462

Abstract

The rapid advancement of Artificial Intelligence (AI) technology is significantly influencing various sectors, including government and industry, by simplifying tasks traditionally performed by humans and creating new opportunities. The objective of this community service initiative was to enhance the technological skills of the caretakers at the Yayasan Santunan Yatim Piatu dan Sosial IRMA in South Jakarta, enabling them to utilize AI for daily activities and organizational needs. This program employed a three-step approach: preparation, implementation, and evaluation. In the preparation phase, challenges faced by the organization in adopting AI were identified, and necessary permissions were secured. The implementation phase involved interactive workshops focusing on computer usage and AI applications, fostering participant engagement through discussions and hands-on activities. Monitoring and evaluation were conducted using questionnaires to assess participant understanding and gather feedback on the training's effectiveness. Results indicated a marked improvement in participants' AI skills, enhancing their organizational capabilities and contributing positively to community development.
Perancangan Sistem Informasi Deteksi Penyakit Daun Padi Menggunakan Metode Agile Faruq Aziz; Daniati Uki Eka Saputri; Nurul Khasanah; Taopik Hidayat
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 8 No. 3 (2025): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v8i3.46970

Abstract

The development of a rice leaf disease detection information system using the Agile method aims to provide an innovative solution for fast and accurate plant disease identification. The system can detect four major rice leaf disease classes: bacterial blight, brownspot, blast, and healthy conditions. The development process follows an iterative approach, starting from understanding user needs to system implementation and testing. Black-box testing was applied to ensure that all features, such as image upload and disease classification, function according to specifications. Evaluation results indicate that the system achieves high accuracy in disease detection based on the utilized dataset. However, dataset limitations and testing scenarios pose challenges for generalizing results to real-field conditions. Hence, intensive evaluation and dataset updates are crucial for future development. With its user-friendly interface, the system is expected to support farmers in improving productivity and efficiency in rice disease detection.
EVALUATING OF DEEP LEARNING MODELS FOR EARLY DETECTION IN MEAT CLASSIFICATION: A STUDY ON BEEF AND PORK DETECTION Taopik Hidayat; Faruq Aziz; Daniati Uki Eka Saputri; Nurul Khasanah
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 7 No. 1 (2026): June 2026
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v7i1.370

Abstract

Accurate classification of beef and pork images is crucial for developing reliable automated food inspection systems, particularly due to their visual similarity in color, texture, and muscle fiber patterns. This study aims to comparatively evaluate the performance of multiple deep learning models for binary meat image classification using RGB digital images. Four Convolutional Neural Network (CNN) architectures, namelyInceptionV3, VGG16, ResNet50, and Xception were assessed under identical preprocessing pipelines and hyperparameter settings to ensure a fair comparison. The dataset underwent cropping, resizing to 224×224 pixels, normalization, and augmentation to enhance variability and improve generalization performance. Model effectiveness was measured using accuracy, precision, recall, and F1-score on unseen test data. Experimental results show that InceptionV3 achieved the most balanced classification performance, with a test accuracy of 72% and an F1-score of 0.7. Although Xception obtained higher training accuracy, it exhibited overfitting during testing, while VGG16 and ResNet50 demonstrated comparatively lower classification capability. These findings indicate that InceptionV3 provides a more stable and generalizable architecture for beef and pork image classification. The study highlights the importance of cross-architecture evaluation in developing robust CNN-based systems for automated meat classification.
Co-Authors Abdul Latif Achmad Baroqah Pohan Achmad Bayhaqy Ade Christian Ade Suryadi Andi Saryoko Aziz, Faruq Bagas Eka Saputra Dama, Desi Masdin Daniati Uki Eka Saputri Dipo Era Ginanti Eka Saputri , Daniati Uki Faruq Aziz Faruq Aziz Faruq Aziz Fitra Septia Nugraha Gata, Windu Hafifah Bella Novitasari Ihsan Aulia Rahman Ilmalia Hartina Irmanti, Kanita Salsabila Dwi Khultsum, Umi M. Rangga Ramadhan Saelan Mulia Rahmayu Naufal Muzakki Ramadhan Nurmalasari Nurmalasari Nurmalasari Nurmalasari Nurul Khasanah Nurul Khasanah Nurwijayanti Pratiwi, Risca Lusiana Rangga Pebrianto Retno Sari Rianggi Silvi Anti Butar-Butar Rina Rina Risca Lusiana Pratiwi Risca Lusiana Pratiwi - Universitas Nusa Mandiri Salsabila, Unik Hanifah Satrio Budi Santoso Septian, Rendi Siswahyudianto Siti Masturoh Siti Masturoh siti nurlela Syarah Seimahuira Tri Rivanie Uki Eka Saputri, Daniati Umi Khultsum Veithzal Rivai Zainal Windu Gata YOANDA, YURIKA PRISILIA