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All Journal Techno.Com: Jurnal Teknologi Informasi Scan : Jurnal Teknologi Informasi dan Komunikasi Proceeding International Conference on Information Technology and Business International conference on Information Technology and Business (ICITB) JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Riset Informatika Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer bit-Tech Journal of Applied Data Sciences Jurnal Ilmiah Teknologi Informasi dan Robotika Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Konstelasi: Konvergensi Teknologi dan Sistem Informasi Journal of Informatics and Computer Science (JINACS) Journal of Emerging Information Systems and Business Intelligence (JEISBI) Inovate : Jurnal Ilmiah Inovasi Teknologi Informasi Smatika Jurnal : STIKI Informatika Jurnal Journal of Applied Informatics Research
SALAMUN ROHMAN NUDIN
Universitas Negeri Surabaya
Religion Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Nursing Public Health Social Sciences

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Implementasi MTCNN dan Transfer Learning Model DeepFace untuk Prediksi Kepribadian Berbasis Video Alamsyah, Shandy Ilham; Nudin, Salamun Rohman
Techno.Com Vol. 24 No. 3 (2025): Agustus 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i3.13084

Abstract

Kepribadian adalah aspek penting yang mempengaruhi pilihan hidup, karir, kinerja, kesehatan, dan juga preferensi atau keinginan seseorang. Model Big-Five Personality adalah yang paling umum, namun pengukurannya masih secara konvensional melalui kuesioner, hal ini memiliki beberapa keterbatasan seperti adanya potensi manipulasi jawaban oleh responden sehingga mempengaruhi hasil dari pengukuran kepribadian tersebut. Untuk mengatasi keterbatasan tersebut, penelitian ini bertujuan untuk mengembangkan sistem untuk melakukan pengukuran atau prediksi kepribadian menggunakan Deep Learning untuk mendeteksi kepribadian berdasarkan ekspresi wajah dalam sebuah video perkenalan. Model yang dikembangkan mencapai akurasi 90.04% dengan loss terendah 9.95%, menunjukkan kemampuannya dalam memprediksi kepribadian secara konsisten. Sistem ini dibangun dengan framework Flask dan mampu menghasilkan prediksi kepribadian seseorang. Dengan demikian penggunakan Deep Learning berpotensi menjadi alat yang efektif dalam pengembangan teknologi di bidang psikologi, menjadikannya alat yang transformatif untuk mengukur kepribadian seseorang dengan lebih efektif di masa depan.   Keywords - Big-Five Personality, Deep Learning, MTCNN, DeepFace, deteksi kepribadian
Prediksi Kepribadian Menggunakan Transfer Learning Model VGG-Face Berbasis Video Arifin, Achmad Nurs Syururi; Nudin, Salamun Rohman
JATISI Vol 12 No 3 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i3.12088

Abstract

This study aims to develop an personality prediction system based on the Big Five Personality model using Transfer Learning with VGG-Face on video data. This research is significant as accurate personality prediction can be applied in various fields, such as behavior analysis. In this study, the pre-trained VGG-Face model, along with two LSTM layers followed by several Dense and Dropout layers, is used for facial feature extraction from video. These features are then used to predict personality across five key dimensions: openness, conscientiousness, extraversion, agreeableness, and neuroticism. The study uses secondary data from the ChaLearn Looking at People (LAP) dataset, which was utilized in the CVPR 2017 competition and includes approximately 10,000 videos. The model is evaluated using the Mean Absolute Error (MAE) metric, which is then converted into regression accuracy. The evaluation results show strong performance with accuracy: Training: 91.75%, Validation: 90.39%, and Testing: 90.28%. The results show that the model has consistency and the ability to generalize well to data it has never encountered before.
Sistem Klasifikasi Tingkat Kesesuaian Bibit Dan Pupuk Dengan Algoritma C4.5 Berbasis Website (Studi Kasus : Kecamatan Megaluh) Panji Sulanggalih, Mochammad; Rohman Nudin, Salamun; Augusta Jannatul Firdaus, Reza
Inovate Vol 6 No 1 (2021): September
Publisher : Fakultas Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33752/inovate.v6i1.3159

Abstract

Selection of suitable seeds and fertilizers will greatly affect the level of plant fertility This research was conducted to classify the types of seeds and fertilizers accordingly, in order to obtain high levels of fertility and crop yields. The attributes used in this study were the type of seed, type of soil, type of pest, type of disease, and type of fertilizer. This study uses the Classification Decision Tree method with the C4.5 Algorithm, which is one of the methods in data mining. This method is used to obtain a set of tree-shaped patterns that can separate data classes from one another, which are used for decision making. The result of this research is a website-based system, so that it can be accessed by all users. This system can be used to classify the appropriate types of seeds and fertilizers based on the rules formed by the C4.5 calculation process. From the test results with the number of training data 499 data, the first root that was formed was fertilizer with a gain value of 0.142. The rule that is formed from the test results is that there are 173 rules that match, 120 rules are very suitable. And from 125 test data, there are 108 correct data and 17 error data, with the correct data percentage is 86.4% and the percentage of error data is 13.6%. Keywords : Classification, Decision Tree, C4.5 Algorithm, Agricultural Seed and Fertilizer, Website.
Deteksi Penyakit pada Tanaman Tomat Menggunakan Model Inception V3 Berbasis Mobile Rahmaditya Putri Lailatul 'Ismi; Salamun Rohman Nudin
Journal of Informatics and Computer Science (JINACS) Vol. 7 No. 02 (2025)
Publisher : Universitas Negeri Surabaya

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

Abstract

Abstrak - Tomat merupakan salah satu komoditas hortikultura yang memiliki tingkat konsumsi tinggi di masyarakat serta berperan penting dalam sektor ekonomi pertanian. Meskipun demikian, produktivitas tanaman tomat kerap mengalami penurunan akibat serangan berbagai penyakit. Terdapat sembilan jenis penyakit utama yang umum menyerang tanaman tomat, yaitu Bacterial Spot, Early Blight, Late Blight, Leaf Mold, Septoria Leaf Spot, Spider Mite, Target Spot, Yellow Leaf Curl Virus, dan Mosaic Virus. Oleh karena itu, upaya deteksi penyakit pada tahap awal menjadi sangat krusial agar petani dapat melakukan langkah pencegahan dan pengendalian secara tepat. Penelitian ini bertujuan untuk mengembangkan sistem pendeteksian penyakit daun tomat berbasis Convolutional Neural Network (CNN) sebagai pendekatan inovatif dalam meningkatkan akurasi identifikasi penyakit. Dataset yang digunakan dalam penelitian ini adalah PlantVillage Dataset, yang terlebih dahulu melalui tahap pra-pemrosesan sebelum dilakukan proses pelatihan menggunakan arsitektur Inception V3 dengan Adam Optimizer. Hasil evaluasi menunjukkan bahwa model yang dikembangkan mampu mencapai tingkat akurasi sebesar 98% dalam mengklasifikasikan jenis penyakit pada daun tomat. Temuan ini mengindikasikan bahwa model Inception V3 memiliki potensi yang sangat baik untuk diimplementasikan sebagai sistem pendukung bagi petani dalam memantau kesehatan tanaman serta meningkatkan kualitas dan kuantitas hasil produksi tomat. Kata Kunci— Tanaman Tomat, Penyakit, Convolutional Neural Network, Inception V3, Mobile.
Personality Prediction Based on Video Using Transfer Learning DeepID Model Handika Dio Pradana; Salamun Rohman Nudin
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2866

Abstract

This research presents an automatic personality prediction system based on the Big Five model openness, conscientiousness, extraversion, agreeableness, and neuroticism by leveraging transfer learning on the DeepID architecture. Video input is first processed with the MTCNN algorithm for robust facial region detection under varying lighting and poses. Extracted features are fed into a modified DeepID model, pre-trained on large-scale face-recognition datasets, to perform spatial encoding. To capture temporal dynamics, Long Short-Term Memory (LSTM) networks model frame-to-frame changes in expression. Training and validation use the ChaLearn LAP dataset of approximately 10,000 annotated videos. Experimental results demonstrate 88.6% overall accuracy, with an average precision of 87.2%, recall of 86.5%, and F1-score of 86.8%, confirming the model’s balanced performance across classes. A minimum loss of 11.3% further underscores effective convergence. The complete pipeline is deployed via Flask, enabling real-time, web-based integration. Beyond academic novelty, this system holds promise for practical applications: in recruitment, it can offer unbiased, rapid personality screening; in mental-health contexts, it may assist clinicians by flagging behavioral cues non-invasively; and in human–computer interaction, adaptive interfaces could personalize responses based on users’ inferred traits. By combining transfer learning with temporal modeling, our approach delivers a scalable, non-invasive tool for automated psychological assessment through visual data, paving the way for ethical, real-time personality analytics in diverse domains.
Efektivitas Media Pembelajaran Berbasis Scratch dalam Meningkatkan Pemahaman Huruf Hijaiyah pada Siswa SD Hang Tuah 7 Surabaya Hafizhuddin Zul Fahmi; Salamun Rohman Nudin; Andi Iwan Nur Hidayat; Asmunin Asmunin; Fisma Meividianugraha Subani; Alvin Febrianto
Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Vol. 6 No. 1 (2026): Januari 2026 - Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/altifani.v6i1.1082

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan meningkatkan pemahaman huruf hijaiyah pada siswa kelas 5 SD Hang Tuah 7 Surabaya melalui inovasi media pembelajaran interaktif berbasis Scratch. Latar belakang penelitian didasari oleh data yang menunjukkan masih rendahnya kemampuan membaca Al-Qur’an di Indonesia. Metode pelaksanaan terdiri atas tiga tahap: persiapan, pelaksanaan pembelajaran menggunakan permainan edukatif Scratch, dan evaluasi melalui pre-test dan post-test. Hasil evaluasi menunjukkan peningkatan pemahaman siswa yang signifikan, dengan rata-rata nilai post-test mencapai 4,71 dari nilai maksimal 5. Analisis statistik menggunakan metode N-Gain menghasilkan nilai 0,90 yang termasuk dalam kategori tinggi, dengan demikian dapat disimpulkan bahwa media pembelajaran berbasis Scratch terbukti efektif tidak hanya dalam meningkatkan pemahaman huruf hijaiyah, tetapi juga dalam menumbuhkan motivasi dan keterlibatan aktif siswa selama proses pembelajaran. 
Implementasi Model MaxVit Untuk Deteksi Penyakit Daun Tanaman Bawang Merah Berbasis Mobile Amanda Khoiromaul Soviyanti; Salamun Rohman Nudin
KONSTELASI: Konvergensi Teknologi dan Sistem Informasi Vol. 6 No. 1 (2026): Juni 2026
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/konstelasi.v6i1.14764

Abstract

Bawang merah (Allium cepa var. aggregatum) merupakan komoditas hortikultura penting di Indonesia. Namun, produktivitasnya sering menurun akibat serangan penyakit daun yang sulit dikenali secara visual. Penelitian ini bertujuan mendeteksi penyakit daun bawang merah menggunakan model Multi-Axis Vision Transformer (MaxViT) dengan teknik transfer learning melalui klasifikasi citra daun. Dataset yang digunakan adalah Onion Dataset yang terdiri dari empat kelas, yaitu Healthy, Purple Blotch, Leaf Blight, dan Iris Yellow Spot Virus. Proses pelatihan model dilakukan menggunakan Python, TensorFlow, dan Google Colab dengan membandingkan optimizer Adam, AdamW, dan SGD. Evaluasi dilakukan menggunakan confusion matrix dengan metrik accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa optimizer Adam menghasilkan performa terbaik dengan akurasi pengujian sebesar 98%. Model terbaik kemudian diimplementasikan ke dalam aplikasi mobile berbasis Flutter untuk mendukung deteksi penyakit daun bawang merah secara cepat dan mudah diakses.
DETECTION OF SUGARCANE LEAF DISEASES USING MOBILENETV3LARGE-BASED TRANSFER LEARNING FOR MOBILE APPLICATIONS Frida Nur Cahyani; Salamun Rohman Nudin
Jurnal Riset Informatika Vol. 8 No. 3 (2026): Juni 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i3.542

Abstract

Sugarcane is one of Indonesia's key plantation commodities with a critical role in fulfilling national sugar demand and supporting bioethanol production. However, sugarcane productivity remains low due to leaf diseases that reduce crop quality and yields, while slow or inaccurate identification accelerates their spread. This study proposes and develops a mobile-based sugarcane leaf disease detection system using transfer learning with the MobileNetV3Large architecture to classify 11 disease classes. Two dataset scenarios were applied: Scenario 1 using the SLD Dataset with 6,748 images and Scenario 2 combining the SLD and Sugarcane Smut datasets totaling 14,804 images. Each scenario was trained under three optimizer configurations: Adam, RMSprop, and SGD, to identify the best-performing combination. Results show that Adam achieved the highest validation accuracy in both scenarios, reaching 94.24% in Scenario 1 and 97.43% in Scenario 2, with corresponding test accuracies of 94.91% and 97.31% respectively. The final model was deployed as a Flutter-based mobile application capable of performing real-time disease detection through image upload or camera capture, providing an accessible tool for farmers to identify sugarcane leaf diseases efficiently.
lmplementasi YOLOv11 untuk Penghitungan Kerumunan Real-Time dalam Arsitektur Microservices pada Video CCTV M.Sultonun Naim; Salamun Rohman Nudin
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 16 No 02 (2026): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v16i02.2378

Abstract

The provision of accurate and easily accessible public information regarding the condition of urban parks remains a challenge in the management of public open spaces. People still do not have a system that allows them to monitor park crowd levels directly, making real-time observation of park conditions difficult. Previous studies on crowd counting have mainly focused on improving object detection performance, while the implementation of crowd monitoring systems as public information services remains limited. Therefore, this research integrates the YOLOv11 algorithm with a microservices architecture to provide real-time crowd information through a public park monitoring website. This research develops a park visitor counting information system based on computer vision using the YOLOv11 algorithm to detect and count crowds from CCTV video streams. The system is designed using a microservices architecture with the FastAPI framework to support real-time detection and data integration into the Surabaya park monitoring website. The research process involves several stages, including dataset preparation, data labeling using Roboflow, YOLOv11 model training, selection of the most optimal optimizer, and implementation of the system on the detection backend. The results show that the YOLOv11m model with the SGD optimizer achieved the best performance, obtaining an mAP@50 score of 92.76%, a recall value of 89.75%, and an F1-score of 90.07%. In addition, the system successfully performed real-time crowd detection and counting under various crowd density levels, lighting conditions, and CCTV camera angles.
Hybrid Transformer-XGBOOST Model Optimized with Ant Colony Algorithm for Early Heart Disease Detection: A Risk Factor-Driven and Interpretable Method Moch Deny Pratama; Faris Abdi El Hakim; Dimas Novian Aditia Syahputra; Dodik Arwin Dermawan; Asmunin Asmunin; Salamun Rohman Nudin; Andi Iwan Nurhidayat
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.969

Abstract

Cardiovascular diseases (CVDs) remain the leading cause of death worldwide, with significant socioeconomic consequences due to premature death and chronic disability. Although clinical screening techniques have evolved, early and accurate prediction of heart disease is still partial due to the limited capacity of conventional machine learning algorithms to model the complex nonlinear interactions among various contributing risk factors e.g., hypertension, diabetes, hyperlipidemia, and genetic predisposition. To address these challenges, this research introduces a hybrid framework that combines the Transformer architecture known for its robust self-attention mechanism and high representational capabilities with Ant Colony Optimization (ACO), a nature-inspired metaheuristic algorithm modeled on the foraging behavior of ants, to enable adaptive and efficient hyperparameter optimization. The proposed model processes structured clinical data by encoding categorical variables into embeddings and normalizing numerical features, resulting in a unified tabular representation suitable for transformer-based analysis. ACO improves model efficiency by optimizing key parameters e.g., embedding configuration, learning rate, and depth, reducing manual intervention and computational overhead. The proposed Hybrid Transformer-ACO model focuses on interpretable clinical features to provide actionable risk stratification. Model evaluation was performed using classification metrics e.g., accuracy, precision, recall, F1 score, and time complexity to measure predictive performance and computational efficiency during the training and inference phases. These evaluation criteria provide evidence of the model's diagnostic reliability, generalizability, and practical feasibility for clinical application.. The model achieved 100% accuracy, sensitivity, specificity, and F1-score, outperforming several models. Time complexity analysis demonstrated efficient training and testing, while the model interpretability supports transparency and trust.
Co-Authors Abdul Khamid Abdul Khamid Adelia Salsabila Arifin Aini Azzah Alamsyah, Shandy Ilham Aldi Naufal Asyadana Alen Bachtiar Heriyanto Alvin Febrianto Amanda Khoiromaul Soviyanti Andi Iwan Nur Hidayat Andi Iwan Nurhidayat andy wildan romadhoni Anggraeni, Erika Kanya Arifin, Achmad Nurs Syururi Asmunin Asmunin Augusta Jannatul Firdaus, Reza Celia Angelina Nogo Koban Chuluq, Khusnul DIMAS NOVIAN ADITIA SYAHPUTRA Dion Danianto Dodik Arwin Dermawan Elok Nur Fauziyah Enggarbela Ogi Intan Pratiwi Enggarbela Ogi Intan Pratiwi Faris Abdi El hakim Fisma Meividianugraha Subani Fitria Hidayanti Fitria Hidayanti Frida Nur Cahyani Ghoffar, Alghifar Abdul Hafizhuddin Zul Fahmi Handika Dio Pradana Laili Indah Liana M.Sultonun Naim Moch Deny Pratama Natanael Chrisanta Rikandi Panji Sulanggalih, Mochammad Prawira, Indra Waskita Prismana, I Gusti Lanang Putra Eka Rahmaditya Putri Lailatul 'Ismi Rama Syaiful Arif Reza, R. BG Moch. Faishal Rifky Fajar Oktanugraha Shinta Yuan Ayu Pratiwi Siti Nur Qholisa SUGIARTO - Sugiarto - Yisti Vita Via Yisti Vita Via