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All Journal Jurnal Sains dan Informatika KOMPUTIKA - Jurnal Sistem Komputer Jurnal Riset Informatika JOISIE (Journal Of Information Systems And Informatics Engineering) Jurnal Teknologi Komputer dan Sistem Informasi Building of Informatics, Technology and Science Jurnal Teknologi Informasi dan Multimedia ILKOMNIKA: Journal of Computer Science and Applied Informatics Jurnal Ilmu Peternakan Terapan JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Jurnal Bumigora Information Technology (BITe) Jurnal Ilmiah Intech : Information Technology Journal of UMUS Among : Jurnal Pengabdian Masyarakat KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Journal Automation Computer Information System (JACIS) Abdiformatika: Jurnal Pengabdian Masyarakat Informatika Formosa Journal of Computer and Information Science JAMU : Jurnal Abdi Masyarakat UMUS Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Jurnal Sains Sistem Informasi Jurnal Pengabdian kepada Masyarakat JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi)
Harliana Harliana
Fakultas Eksakta, Universitas Nahdlatul Ulama (UNU) Blitar
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Comparative Analysis of Traditional Machine Learning and Sequential Deep Learning Models for Spam Email Classification Harliana Harliana; Hartatik Hartatik; Achmad Alvi Yudanuari
Formosa Journal of Computer and Information Science Vol. 5 No. 1 (2026): March 2026
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjcis.v5i1.16502

Abstract

This study compares the performance of traditional machine learning methods and sequential deep learning models for text-based spam classification. The primary issue addressed is the lack of consistent, fair evaluation across these approaches due to variations in datasets, preprocessing techniques, and experimental settings across previous studies. To overcome this limitation, this research proposes a controlled comparative evaluation framework by employing a unified dataset, standardized preprocessing procedures, consistent data splitting, and identical evaluation metrics. The dataset used consists of 5,572 messages with an imbalanced class distribution; therefore, oversampling was applied to the training data to mitigate bias. The evaluated models include TF-IDF-based Logistic Regression as the baseline, as well as Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs) as deep learning models.
Pemanfaatan Generatif AI untuk Promosi Visual Usaha Kuliner Rumahan "Oma Rusmini" Harliana Harliana; Yuniar Alam; RDR Yusron; Tito Prabowo
Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Vol. 5 No. 4 (2025): Juli 2025 - Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/3wtbcj65

Abstract

Penelitian ini bertujuan memberikan pelatihan kepada pelaku usaha “Oma Rusmini” dalam memanfaatkan teknologi Generatif AI untuk merancang konten promosi, deskripsi produk, serta skrip video agar lebih menarik dan disesuaikan dengan karakteristik target pasar dan membantu mengevaluasi dampak akan penggunaan Generatif AI terhadap peningkatan efektivitas pemasaran produk pada media sosial dan platform online yang dimiliki pelaku usaha “Oma Rusmini”. Upaya yang dilakukan peneliti memiliki dampak peningkatan signifikan dalam prosentase kata unik, dengan rata-rata mencapai 60% pada setiap caption. Hal ini menunjukkan kosakata yang digunakan lebih bervariatif sehingga skor pembuka memperoleh nilai 3 karena langsung menarik perhatian dengan kata khas yaitu “oma” dan “pedasnya nendang”.
PERFORMANCE COMPARISON OF MUSHROOM TYPE CLASSIFICATION BASED ON MULTI-SCENARIO DATASET USING DECISION TREE C4.5 AND C5.0 Citra Mirna Wati; Abd. Charis Fauzan; Harliana Harliana
Jurnal Riset Informatika Vol. 4 No. 3 (2022): June 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v4i3.173

Abstract

Indonesia has a tropical climate that supports mushroom growth. Mushroom classification into poisonous and non-poisonous mushrooms. Identification of the type of mushroom is vital because mushrooms, especially poisonous mushrooms, risk causing potential hazards to humans, such as causing serious illness and even death. This study aimed to identify the fungus type using a computational approach, namely the Decision Tree C4.5 and C5.0 Algorithms. This research contributes to using multi-scenario datasets and comparing the performance of the C4.5 and C5.0 decision tree algorithms. The dataset used is a fungal classification dataset obtained from kaggle.com. The method stages in this research are literature study, data collection, and data preprocessing, which includes a data cleaning process and a partitioning process for multi-scenario datasets. Afterwards, the Decision Tree Algorithms C4.5 and C5.0 were implemented using the sci-kit-learn library. The last step is to do a performance comparison using the confusion matrix. The results showed that identifying poisonous mushrooms using the Decision Tree C5.0 Algorithm obtained an accuracy of 97.05% for scenario 1, 97.00% for scenario 2, and 97.11% for scenario 3. At the same time, the Decision Tre C4.5 algorithm yielded an accuracy. by 96.92% for scenario 1, 96.90% for scenario 2, and 97.05% for scenario 3. Based on the comparison of the performance of the classification results, we conclude that the Decision Tree C5.0 algorithm in scenario 3 has the highest accuracy for fungal identification poisonous.
Perbandingan Logistic Regression dan Random Forest untuk Prediksi Respon Pelanggan Asuransi Harliana Harliana; Tito Prabowo; Ady Alzhava Nuary
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 24 No 2 (2026): Mei 2026
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v24i2.214

Abstract

Vehicle insurance companies increasingly rely on data-driven marketing strategies to identify prospective customers who are likely to respond positively to insurance offers. However, customer response prediction is challenging due to class imbalance, where non-responsive customers substantially outnumber responsive ones. This study aims to compare the performance of Logistic Regression and Random Forest models in predicting customer responses to vehicle insurance products using the Synthetic Minority Oversampling Technique (SMOTE). The analysis was conducted using the Vehicle Insurance dataset obtained from Kaggle. Experimental results indicate that Random Forest achieved the best overall performance, with an accuracy of 0.80, a positive-class F1-score of 0.59, and a ROC–AUC score of 0.88. In contrast, Logistic Regression produced a higher positive-class recall of 0.98 but a lower precision of 0.35, indicating a greater tendency to generate false-positive predictions. Feature importance analysis revealed that Previously_Insured, Vehicle_Damage, and Age were the most influential factors affecting customer responses. These findings suggest that the combination of Random Forest and SMOTE provides an effective approach for handling imbalanced data and improving customer response prediction in vehicle insurance marketing campaigns.
Co-Authors Abd. Charis Fauzan Achmad Alvi Yudanuari Ady Alzhava Nuary Andri Syafrianto Anggara Setya Saputra Ayu Eviana Citra Mirna Wati Deny Restyo Nugroho Dewi Anggraini Dewi Anggraini Dewi Lestari Dwi Nurani FATRA NONGGALA PUTRA Fery Sofian Efendi Fiqih Ainul Qhabib Gian Fitralisma Gunawan Gunawan Hartatik Hasanah, Niswatin Imam Machfud Indra Riyana Rahadjeng Intan Nur Fauziyah Izzudin, Ahmad Kafi, Elfiya Khanatul Kartika Dewi Khalid Iskandar Khoiril Hikmah Khoirul Muanam, Mohammad Kirono, Sodik Leni Ayu Marlina Lestari, Febbi Sena Luthfi Hardiansyah Mei Maemunah Muhamad Bagas Gigih Muhamat Maariful Huda Muhammat Maariful Huda Mukidin Mukidin Nining Haryuni Nurul Aziz Tri Wahyuni Nurul Baety Tsani Otong Saeful Bachri Prabowo, Tito Putri Handayani R.M.Herdian Bhakti Rahmadita Sabrina Islahany RDR Yusron Rizqi Darma Rusdiyan Yusron Roby Setiadi Rusdian Yusron, Rizki Darma Salnan Sabdo Wibowo Setyawan, Dimas Ari Sikone, Hilarius Yosef Siti Nuralia Subana Subana Suraji Suraji Titi Rahmawati Tito Prabowo Ulfa Nurfitria Sugandi Winanjaya, Riki Wiwi Widayani Yuniar Alam Yuniar Alam, Yuniar