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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) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Riset Informatika bit-Tech Journal of Applied Data Sciences Jurnal Ilmiah Teknologi Informasi dan Robotika Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Journal of Informatics and Computer Science (JINACS) Journal of Emerging Information Systems and Business Intelligence (JEISBI) Inovate : Jurnal Ilmiah Inovasi Teknologi Informasi
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 Engineering Languange, Linguistic, Communication & Media Library & Information Science Nursing Public Health Social Sciences

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Riset Operasional Berbasis Permainan Android dengan Metode Simplex pada UD. Dieva Cake Ghoffar, Alghifar Abdul; Nudin, Salamun Rohman
Journal of Emerging Information Systems and Business Intelligence Vol. 3 No. 1 (2022)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v3i1.44166

Abstract

Dieva Cake merupakan perusahaan yang bergerak dibidang produksi kue dan jajanan tradisional. Proses perhitungan pada produksi UD. Dieva Cake mengunakan cara pembagian sederhana tanpa adanya perhitungan dalam jumlah produksi tiap kue. Pemilik perusahaan tidak mengetahui metode untuk perhitungan keuntungan maksimal dan pada proses penerapan metode tersebut perusahaan kesulitan dikarenakan masih terlalu awam dengan metode perhitungan keuntungan maksimal. Hal ini menyebabkan setiap proses produksi perusahaan tidak mengetahui apakah keuntungannya sudah optimal atau tidak.Penelitian ini mengimplementasikan metode perhitungan keuntungan maksimal berupa metode simpleks dan untuk mengatasi keterbatasan pengguna dalam menggunakan metode tersebut maka penelitian ini menghasilkan program dengan bentuk aplikasi permainan agar mudah diterima oleh perusahaan. Berdasarkan target pengguna, metode simpleks yang berbentuk tabel dan rumus dibuat dengan model dialog. Pengguna hanya tinggal menjawab pertanyaan dan hasil perhitungan keuntungan maksimal akan muncul. Terdapat juga soal cerita untuk menggambarkan bagaimana metode simpleks diimplementasikan. Percobaan penggunaan aplikasi ini pada lima level permainan mendapatkan hasil seluruh perhitungan akurat dimana perhitungan yang tepat tersebut menjadi jawaban di setiap level permainan. Dari uji coba yang dilakukan untuk menghitung 5 pertanyaan cerita pada aplikasi mendapatkan hasil yang benar pada seluruhan uji coba. Dari uji coba pengguna dengan subjek tes pemilik perusahaan dan karyawan menggunakan skala likert mendapatkan nilai penerimaan pengguna dengan persentase rata rata sebesar 79,44%.
Analisis Kepuasan Pengguna Aplikasi JConnect Mobile Menggunakan Metode End User Computing Satisfaction (EUCS) dan Importance Performance Analysis (IPA) Qholisa, Siti Nur; Nudin, Salamun Rohman
Journal of Emerging Information Systems and Business Intelligence Vol. 4 No. 2 (2023)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v4i2.54974

Abstract

Pengembangan Sistem Informasi Persediaan Barang Di Cv. Nusantara List Supplay Menggunakan Metode FIFO Berbasis Website Dengan Framework Laravel Asyadana, Aldi Naufal; Nudin, Salamun Rohman
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 5 No. 1 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v5i1.58935

Abstract

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.
Hybrid Transformer-XGBOOST Model Optimized with Ant Colony Algorithm for Early Heart Disease Detection: A Risk Factor-Driven and Interpretable Method Pratama, Moch Deny; El Hakim, Faris Abdi; Aditia Syahputra, Dimas Novian; Dermawan, Dodik Arwin; Asmunin, Asmunin; Nudin, Salamun Rohman; Nurhidayat, Andi Iwan
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.
TRANSFER LEARNING WITH EFFFICIENTNET-B0 FOR CAT BREED CLASSIFICATION: A COMPARATIVE EVALUATION OF OPTIMIZERS Azzah, Aini; Salamun Rohman Nudin
Jurnal Riset Informatika Vol. 8 No. 1 (2025): Desember 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (989.569 KB) | DOI: 10.34288/jri.v8i1.417

Abstract

Cats are widely kept as companion animals and exhibit substantial breed level variation in appearance and behavior that influences their care. This study develops a lightweight, image based classifier for identifying twelve common cat breeds using transfer learning on the EfficientNet-B0 backbone. Experiments contrasted four optimization algorithms (SGD, AdaGrad, RMSProp, and Adam) to identify the training strategy that balances convergence speed and generalization. Model effectiveness was measured with confusion matrix analysis and common classification indicators (accuracy, precision, recall, and F1-score). The best performing setup, EfficientNet-B0 fine tuned with the Adam optimizer attained 92% training accuracy, 89% validation accuracy, and 88% on the held out test partition. Subsequently, we integrated the trained model into a Flask web application, backed by an SQLite database, and conducted black-box testing to assess its functional reliability. All system functions met specifications and runtime predictions corresponded closely to ground truth labels. This platform provides a rapid and accurate tool for cat owners and enthusiasts to identify breeds in real-world scenarios, highlighting the usefulness of transfer learning in a streamlined web based implementation.
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.
Co-Authors Abdul Khamid Abdul Khamid Aditia Syahputra, Dimas Novian Alamsyah, Shandy Ilham Alen Bachtiar Heriyanto Alvin Febrianto Andi Iwan Nur Hidayat Andi Iwan Nurhidayat Anggraeni, Erika Kanya Arifin, Achmad Nurs Syururi Asmunin Asmunin Asyadana, Aldi Naufal Augusta Jannatul Firdaus, Reza Azzah, Aini Celia Angelina Nogo Koban Chuluq, Khusnul Dodik Arwin Dermawan Elok Nur Fauziyah Enggarbela Ogi Intan Pratiwi Enggarbela Ogi Intan Pratiwi Faris Abdi El Hakim, Faris Abdi Fisma Meividianugraha Subani Fitria Hidayanti Fitria Hidayanti Ghoffar, Alghifar Abdul Ghoffar, Alghifar Abdul Hafizhuddin Zul Fahmi Handika Dio Pradana Laili Indah Liana Natanael Chrisanta Rikandi Panji Sulanggalih, Mochammad Pratama, Moch Deny Prawira, Indra Waskita Prismana, I Gusti Lanang Putra Eka Qholisa, Siti Nur Rahmaditya Putri Lailatul 'Ismi Rama Syaiful Arif Rifky Fajar Oktanugraha Shinta Yuan Ayu Pratiwi SUGIARTO - Sugiarto - Yisti Vita Via Yisti Vita Via