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All Journal JSM (Jurnal SIFO Mikroskil) Jurnal Ilmu Komputer dan Agri-Informatika IJISTECH (International Journal Of Information System & Technology) Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal JSR : Jaringan Sistem Informasi Robotik Jurnal Sistem Komputer & Kecerdasan Buatan Jurnal Sistem Komputer dan Informatika (JSON) Innovation in Research of Informatics (INNOVATICS) IJISTECH JoMMiT : Jurnal Multi Media dan IT Decode: Jurnal Pendidikan Teknologi Informasi JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Sains Informatika Terapan (JSIT) Jurnal abimana (Jurnal Pengabdian Kepada Masyarakat Nasional) Jurnal Indonesia Mengabdi
Dani Rofianto
Politeknik Negeri Lampung
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Performance Evaluation Of SVM With Parameter Optimization On Credit Card Fraud Data Subset Using SMOTE Mahardika, Ahmad Farrel; Fahrezi, Irza Nuzul; Alleredha, Muhammad Hadya; Amaliah, Khusnatul; Rofianto, Dani
IJISTECH (International Journal of Information System and Technology) Vol 9, No 1 (2025): The June Edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v9i1.398

Abstract

This study evaluates the performance of the Support Vector Machine (SVM) algorithm in detecting credit card fraud by overcoming the class imbalance problem using the Synthetic Minority Oversampling Technique (SMOTE) technique and parameter optimization through Grid Search. The dataset used is sourced from Kaggle, consists of 10,001 transactions, and has been balanced. SMOTE is applied exclusively to the training data to prevent data leakage. The optimization process produces the best parameters at a value of C = 10 and gamma = 0.1. Model evaluation is carried out using recall, precision, F1-score, and AUC-ROC metrics. The results show a significant increase in performance in recognizing fraudulent transactions. The final model recorded a recall of 0.68, precision 0.90, F1-score 0.77, and AUC-ROC 0.98. These findings prove that the combination of SMOTE techniques and parameter optimization can improve the effectiveness of SVM in classifying minority classes more accurately. This approach is considered to have great potential to be applied in automated fraud detection systems in the financial sector.
Prediksi Kekambuhan Kanker Tiroid Menggunakan Algoritma Random Forest Safitri, Egi; Rofianto, Dani; Karnila, Sri; Nurjoko, Nurjoko; Kurniawan, Hendra; Arkhiansyah, Yuni; Rizal, Ruki
Jurnal SISKOM-KB (Sistem Komputer dan Kecerdasan Buatan) Vol. 8 No. 3 (2025): Volume VIII - Nomor 3 - Mei 2025
Publisher : Teknik Informatika, Sistem Informasi dan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47970/siskom-kb.v8i3.833

Abstract

Kekambuhan kanker tiroid pasca terapi Radioactive Iodine (RAI) merupakan tantangan penting dalam penatalaksanaan jangka panjang pasien. Penelitian ini bertujuan membangun model prediktif untuk mengidentifikasi potensi kekambuhan dengan memanfaatkan data klinis dan patologis menggunakan algoritma Random Forest. Dataset terdiri atas 383 data pasien dengan 13 atribut, termasuk usia, jenis kelamin, staging kanker, jenis patologi, klasifikasi risiko, dan respons terhadap terapi. Proses pra-pemrosesan meliputi penyandian data kategorik, eksplorasi fitur, dan pembagian data latih dan uji secara stratifikasi. Hasil evaluasi menunjukkan performa tinggi dari model, dengan akurasi 96,5%, presisi 96,7%, recall 90,6%, dan AUC 0,99. Analisis fitur menggunakan SHAP mengungkap bahwa Stage, Response, dan Risk merupakan faktor paling berkontribusi terhadap prediksi kekambuhan. Penelitian ini menunjukkan bahwa model Random Forest tidak hanya efektif dalam klasifikasi biner, tetapi juga dapat diinterpretasikan secara klinis untuk mendukung pengambilan keputusan medis yang lebih personal dan preventif.
Analysis of Climate Change on Agricultural Yields with Principal Component Analysis and Linear Regression Approaches Safitri, Egi; Rofianto, Dani
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 13, No 3 (2025)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v13i3.91434

Abstract

Climate Change has become a global issue that significantly impacts various sectors, including agriculture. This study aims to analyze the influence of climate variables, such as average temperature, rainfall, carbon dioxide (CO2) emissions, and extreme weather events on agricultural yields. The Principal Component Analysis (PCA) method was used to identify the main variability patterns in the data. At the same time, multiple linear regression was applied to determine the relationship between climate variables and crop yields. The analysis showed that temperature and precipitation were the main factors affecting agricultural yields, with increases in temperature being negatively correlated to crop productivity. PCA identified two principal components that explained the variability in the data, while multiple linear regression showed that temperature and extreme weather events significantly affected crop yields. The results underscore the potential of adaptation strategies in the agricultural sector, such as using climate-resilient crop varieties, water resource management efficiency, and agricultural technology innovation to increase resilience to climate change.
Penerapan Algoritma Random Forest dalam Prediksi Emosi Musik Berdasarkan Karakteristik Fitur Audio Spotify Marsya, Nabila Defany; Munadhil, Muhammad Mufrih; Dzakyananta, M Alvin; Amaliah, Khusnatul; Rofianto, Dani
Jurnal Sains Informatika Terapan Vol. 4 No. 2 (2025): Jurnal Sains Informatika Terapan (Juni, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v4i2.648

Abstract

Emotion classification in music is a crucial aspect in developing context-aware recommendation systems that respond to the listener’s mood. The Random Forest algorithm is used to map song emotions based on Spotify audio features, namely valence, energy, loudness, and danceability, which reflect the psychological and acoustic aspects of music. The dataset was collected through the Spotify Web API and public repositories, consisting of songs released between 2009 and 2019. Data processing involved normalization and labeling emotions into five categories: Angry, Calm, Happy, Neutral, and Sad. The model was trained using 70% of the data and tested with the remaining 30%. Evaluation results showed an accuracy of 98.75%, with perfect F1-scores for the Happy and Sad categories. Valence and energy were found to be the most influential features, while Calm was often confused with Neutral due to similar acoustic patterns. These findings demonstrate that the Random Forest approach is effective in accurately and consistently classifying music emotions based on audio features.
Model Ensembel untuk Deteksi Depresi di Twitter Berbahasa Indonesia Fitri, Melisa; Nurkhotimah, Jihan Susan; Ihsan, Faiz Nurfaadhil; Amaliah, Khusnatul; Rofianto, Dani
Jurnal Sifo Mikroskil Vol. 26 No. 2 (2025): JSM VOLUME 26 NOMOR 2 TAHUN 2025
Publisher : Fakultas Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55601/jsm.v26i2.1812

Abstract

Pentingnya Deteksi dini gangguan kesehatan mental khususnya depresi di era digital saat ini di mana individu lebih cenderung mengekspresikan kondisi emosionalnya melalui media sosial. Penelitian ini bertujuan untuk mengembangkan model ensembel Machine Learning dalam mendeteksi gejala depresi pada postingan media sosial berbahasa Indonesia, khususnya dari platform Twitter. Dataset yang digunakan adalah Depression and Anxiety in Twitter (ID) yang terdiri dari 6.980 teks berlabel. Proses preprocessing mencakup pembersihan data, vektorisasi dengan TF-IDF, dan pemisahan data menggunakan metode overfitting. Empat algoritma yaitu Support Vector Machine, Naïve Bayes, Random Forest, dan AdaBoost dikombinasikan menggunakan Voting Classifier dengan pendekatan soft voting. Evaluasi model dilakukan menggunakan metrik akurasi, precision, recall, dan F1-score, serta visualisasi heatmap korelasi dan learning curve. Hasil menunjukkan bahwa model Voting Classifier menghasilkan kinerja terbaik dengan F1-score makro sebesar 0,996, menunjukkan bahwa pendekatan ensembel efektif dalam meningkatkan akurasi dan stabilitas klasifikasi. Penelitian ini berkontribusi dalam pengembangan sistem deteksi dini gangguan mental berbasis teks bahasa Indonesia yang dapat digunakan oleh lembaga kesehatan, institusi pendidikan, dan organisasi sosial.
Metode Ekstraksi Fitur dan Klasifikasi Visual Untuk Identifikasi Kualitas Pangan Lokal Berbasis Citra Digital Rofianto, Dani; Amaliah, Khusnatul; Khoerunissa, Tiara Kurnia; Fitri, Melisa
Jurnal Ilmu Komputer dan Agri-Informatika Vol. 12 No. 2 (2025)
Publisher : Sekolah Sains Data, Matematika, dan Informatika. Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.12.2.178-188

Abstract

Mutu pangan lokal memiliki peran penting dalam menjaga ketahanan pangan dan meningkatkan daya saing produk di pasar. Namun, penentuan mutu masih mengandalkan inspeksi visual manual yang bersifat subjektif dan tidak konsisten, sehingga berpotensi menimbulkan kerugian pascapanen serta menurunkan kepercayaan konsumen. Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi mutu pangan berbasis citra digital dengan memanfaatkan arsitektur deep learning EfficientNetV2B0 melalui pendekatan transfer learning. Dataset yang digunakan mencakup delapan kelas dari empat komoditas utama, yaitu apel, pisang, tomat, dan pare dalam kondisi segar dan tidak segar. Seluruh citra diproses melalui tahap prapengolahan berupa pengubahan ukuran dan normalisasi, serta dilakukan augmentasi data untuk meningkatkan variasi dan mencegah overfitting. Model dilatih dengan konfigurasi ringan dan dievaluasi menggunakan akurasi, presisi, recall, F1-score, serta confusion matrix. Hasil penelitian menunjukkan bahwa model mampu mencapai akurasi keseluruhan sebesar 99.8 persen dengan nilai presisi, recall, dan F1-score mendekati sempurna pada hampir semua kelas. Temuan ini membuktikan bahwa penerapan transfer learning dan augmentasi data efektif dalam membedakan pangan segar dan tidak segar berdasarkan citra digital. Secara praktis, sistem ini berpotensi mendukung petani dan pelaku usaha kecil menengah dalam melakukan inspeksi mutu yang lebih objektif, efisien, dan konsisten.
Deteksi Dini Penyakit Tanaman Jagung Berbasis Transfer Learning dengan Arsitektur DenseNet121 Rofianto, Dani; Maulini, Rima; Sahlinal, Dwirgo; Meilantika, Dian; Pujiana, Tri
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i2.9094

Abstract

Jagung (Zea mays L.) merupakan komoditas pangan strategis di Indonesia yang berperan penting dalam ketahanan pangan, industri pakan ternak, hingga energi terbarukan. Produktivitas jagung kerap menurun akibat penyakit daun seperti Blight, Common Rust, dan Gray Leaf Spot, yang dapat mengurangi hasil panen hingga 30–50% jika tidak dideteksi sejak dini. Metode deteksi konvensional melalui pengamatan visual masih memiliki keterbatasan, antara lain subjektivitas penilaian, kurangnya tenaga ahli, serta keterlambatan dalam pengambilan keputusan. Oleh karena itu, diperlukan pendekatan berbasis kecerdasan buatan yang mampu melakukan deteksi secara cepat, akurat, dan efisien di lapangan. Penelitian ini mengusulkan penggunaan transfer learning dengan arsitektur DenseNet121 untuk klasifikasi penyakit daun jagung. Dataset yang digunakan terdiri dari 2.818 citra yang terbagi ke dalam empat kelas utama (Blight, Common Rust, Gray Leaf Spot, Healthy), diperoleh dari kombinasi dokumentasi lapangan dan dataset terbuka daring. Data kemudian dibagi menggunakan stratified split menjadi 68% latih, 17% validasi, dan 15% uji. Hasil pengujian menunjukkan bahwa model mencapai akurasi 93,48% dengan F1-score rata-rata 0,93. Kelas Healthy dan Common Rust teridentifikasi hampir sempurna, sementara kesalahan klasifikasi masih ditemukan pada Gray Leaf Spot yang sering terprediksi sebagai Blight. Kurva akurasi dan loss memperlihatkan dinamika pelatihan yang stabil tanpa indikasi overfitting, berkat penerapan augmentasi data, dropout, dan early stopping. Temuan ini menegaskan bahwa DenseNet121 berpotensi besar untuk diterapkan dalam sistem deteksi dini penyakit jagung berbasis AI, sekaligus mendukung pengembangan pertanian presisi dan peningkatan produktivitas nasional.
Penguatan Ekonomi KWT Srikandi Melalui Optimalisasi Digital Marketing Dan Diversifikasi Produk Olahan Cabai di Pringsewu Amaliah, Khusnatul; Dani Rofianto; Tiara Kurnia Khoerunnisa; Fitri, Melisa; Armelsya, Kafka; Sekarsari, Ni Wayan Sevilla
Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Vol. 9 No. 1 (2026): Januari 2026
Publisher : STMIK Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurdimas.v9i1.4228

Abstract

Abstract: This community service activity aims to strengthen the economy of the Srikandi Women Farmers Group (KWT) in Pekon Srikaton, Adiluwih District, Pringsewu Regency, through the implementation of digital marketing and diversification of processed chili products. The main problems of KWT include limited skills in chili processing and low utilization of digital technology for marketing. The method used is Participatory Action Research (PAR), with stages of socialization, chili processing training, digital marketing training, mentoring, and evaluation. The training activities increased the skills of KWT members in processing chili peppers into various products such as chili flakes, chili powder, and chili oil, as well as improving their ability to utilize social media for promotion. The results of the evaluation showed a significant increase in technical skills, entrepreneurial interest, and motivation of members to develop a business based on chili derivative products. This activity has proven to be effective in encouraging the economic independence of women through the use of technology and product innovation. Keywords: chili; digital marketing; product diversification Abstrak: Kegiatan pengabdian kepada masyarakat ini bertujuan untuk memperkuat ekonomi Kelompok Wanita Tani (KWT) Srikandi di Pekon Srikaton, Kecamatan Adiluwih, Kabupaten Pringsewu, melalui penerapan digital marketing dan diversifikasi produk olahan cabai. Permasalahan utama mitra meliputi keterbatasan keterampilan dalam pengolahan cabai serta rendahnya pemanfaatan teknologi digital untuk pemasaran. Metode yang digunakan adalah Participatory Action Research (PAR), dengan tahapan sosialisasi, pelatihan pengolahan turunan cabai, pelatihan digital marketing, pendampingan, serta evaluasi. Kegiatan pelatihan menghasilkan peningkatan keterampilan anggota KWT dalam mengolah cabai menjadi berbagai produk seperti bon cabai, cabai bubuk, dan chili oil, serta meningkatkan kemampuan dalam memanfaatkan media sosial untuk promosi. Hasil evaluasi menunjukkan adanya peningkatan signifikan dalam kemampuan teknis sebesar 60%, minat berwirausaha, dan motivasi anggota untuk mengembangkan usaha berbasis produk turunan cabai sebesar 20%. Kegiatan ini terbukti efektif dalam mendorong kemandirian ekonomi perempuan melalui pemanfaatan teknologi dan inovasi produk. Kata kunci: cabai; digital marketing; diversifikasi produk
Sistem Irigasi dan Pemupukan Cerdas Berbasis IoT dengan Integrasi Cloud Database Dani Rofianto; Eko Win Kenali; Khusnatul Amaliah; Jaka Fitra; Halim Fathoni; Tiara Kurnia Khoerunnisa; Hevia Purnama Sari
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 15 No 1: Februari 2026
Publisher : This journal is published by the Department of Electrical and Information Engineering, Faculty of Engineering, Universitas Gadjah Mada.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v15i1.23124

Abstract

Internet of Things (IoT)-based smart agriculture provides an innovative solution to enhance the efficiency and sustainability of agricultural production amid challenges such as water scarcity, inefficient fertilization, and climate variability. This study developed an IoT-based smart irrigation and fertilization management system integrated with the Firebase Realtime Database for real-time monitoring and control. The system combined soil moisture, air humidity, and temperature sensors with an ESP32 microcontroller, enabling automatic and manual decision-making based on environmental conditions. Users could interact with the system via a responsive web dashboard that provided both data visualization and manual control. System testing conducted in a greenhouse environment demonstrated stable and accurate data acquisition, with average readings of 27.91°C for temperature, 74.75% RH for air humidity, and 71.31% for soil moisture, within ±2.3% of analogue measurements. The relay actuation response time was less than 1 s, while Firebase synchronization achieved over 98% reliability during continuous operation. Additionally, the system achieved 20% water savings compared to manual irrigation methods and successfully controlled fertilizer distribution and exhaust ventilation to stabilize humidity. These results confirm that the proposed system supports real-time, precise, and energy-efficient control, suitable for small to medium-scale agricultural applications, especially in areas with unstable internet connectivity. This research establishes a strong foundation for future integration with AI-based systems, such as fuzzy logic and machine learning, to enable fully autonomous, adaptive precision agriculture.
Co-Authors - Nurjoko Alleredha, Muhammad Hadya Amaliah, Khusnatul Armelsya, Kafka Astria Hijriani, Astria Dewi Kania Widyawati Dwirgo Sahlinal, Dwirgo Dzakyananta, M Alvin Eko Win Kenali Fahrezi, Irza Nuzul Fathurrahman Kurniawan Ikhsan Fitra, Jaka Fitri, Melisa Halim Fathoni Halim Fathoni Hendra Kurniawan Hevia Purnama Sari Ihsan, Faiz Nurfaadhil K Ikshan, Fathurrahman Khoerunissa, Tiara Kurnia Mahardika, Ahmad Farrel Marsya, Nabila Defany Maulini, Rima meilantika, Dian Munadhil, Muhammad Mufrih Neni Purwati Nurkhotimah, Jihan Susan Oki Arifin Pujiana, Tri Rizal, Ruki Safitri, Egi Sekarsari, Ni Wayan Sevilla Sri Karnila Tiara Kurnia Khoerunnisa Yuni Arkhiansyah