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All Journal Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Informatika Universitas Pamulang J-SAKTI (Jurnal Sains Komputer dan Informatika) JURTEKSI JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal ICT : Information Communication & Technology JSAI (Journal Scientific and Applied Informatics) Jurnal Informatika dan Rekayasa Elektronik JATI (Jurnal Mahasiswa Teknik Informatika) Respati Jurnal Teknika JIKA (Jurnal Informatika) Jurnal Teknik Informatika (JUTIF) JNANALOKA Journal of Electrical Engineering and Computer (JEECOM) Information System Journal (INFOS) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) International Journal of Artificial Intelligence and Robotics (IJAIR) Jurnal Pendidikan dan Teknologi Indonesia J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Informatika Teknologi dan Sains (Jinteks) Duta.com : Jurnal Ilmiah Teknologi Informasi dan Komunikasi Journal of Comprehensive Science Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Jurnal Bangkit Indonesia Jurnal Pendidikan Indonesia (Japendi) Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Intechno Journal : Information Technology Journal The Indonesian Journal of Computer Science International Journal of Information Engineering and Science
DHANI ARIATMANTO
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Social Sciences Transportation Other

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AHP-TOPSIS AND ANOVA METHOD APPROACH IN SOFTWARE DEVELOPMENT CRITERIA SELECTION ACCORDING TO ISO 12207:2017 Fadilla, Rizqi Mirza; Ariatmanto, Dhani
JURTEKSI (Jurnal Teknologi dan Sistem Informasi) Vol 11, No 3 (2025): Juni 2025
Publisher : Universitas Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i3.3698

Abstract

Abstract: The rapid development of information technology has increased the demand for high-quality software, necessitating a structured development process. ISO/IEC/IEEE 12207:2017 serves as an international standard encompassing organizational, technical, and project support processes, differing from ISO 9001, which focuses more generally on quality management. This study employs a Multi-Criteria Decision Making (MCDM) approach by integrating the Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). AHP determines the weight of ISO 12207:2017 criteria through pairwise comparisons, while TOPSIS ranks software development activities based on these weights. To validate the results, Analysis of Variance (ANOVA) is applied. The findings indicate that the Software Requirements Definition Process has the highest priority weight (0.169), followed by Implementation (0.101) and Operation (0.095). Software Configuration Management is identified as the most critical activity with the highest TOPSIS score (0.221). ANOVA confirms the reliability of expert evaluations, showing no significant differences. This study provides a structured decision-making framework based on ISO 12207:2017, helping optimize software project management while ensuring alignment with international standards and industry best practices.            Keywords: AHP; TOPSIS; ANOVA; ISO 12207:2017  Abstrak: Perkembangan teknologi informasi meningkatkan permintaan perangkat lunak berkualitas tinggi, sehingga diperlukan proses terstruktur dalam pengembangannya. ISO/IEC/IEEE 12207:2017 menjadi standar internasional yang mencakup proses organisasi, teknis, dan pendukung proyek, berbeda dengan ISO 9001 yang lebih umum pada manajemen kualitas. Penelitian ini menggunakan Multi-Criteria Decision Making (MCDM) dengan mengintegrasikan Analytic Hierarchy Process (AHP) dan Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). AHP menentukan bobot kriteria ISO 12207:2017 melalui perbandingan berpasangan, sementara TOPSIS memeringkat aktivitas pengembangan berdasarkan bobot tersebut. Untuk validasi, Analysis of Variance (ANOVA) diterapkan. Hasil penelitian menunjukkan bahwa Proses Definisi Kebutuhan Perangkat Lunak memiliki bobot tertinggi (0,169), diikuti Implementasi (0,101), dan Operasi (0,095). Manajemen Konfigurasi Perangkat Lunak menjadi aktivitas paling kritis dengan skor TOPSIS tertinggi (0,221). ANOVA mengonfirmasi keandalan penilaian para ahli tanpa perbedaan signifikan. Penelitian ini memberikan kerangka kerja pengambilan keputusan berbasis ISO 12207:2017, membantu optimalisasi manajemen proyek perangkat lunak, serta memastikan keselarasan dengan standar internasional dan praktek terbaik industri. Kata kunci: AHP; TOPSIS; ANOVA; ISO 12207:2017
The Impact of Feature Extraction in Random Forest Classifier for Fake News Detection Ariatmanto, Dhani; Rifai, Anggi Muhammad
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 6 (2024): December 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i6.6017

Abstract

The pervasive issue of fake news spreading rapidly on online platforms. causing a concerning dissemination of misinformation. The influence of fake news has become a pressing social problem, shaping public opinion in important events such as elections. This research focuses on detecting and classifying fake news using the Random Forest algorithm by investigating the impact of feature extraction techniques on classification accuracy, this study specifically employs the TF-IDF method. For this purpose, we used 44,898 English-language articles from the ISOT fake news dataset. The dataset is cleaned using tokenization and stemming then split into 75% training and 25% testing. The TF-IDF vectorizer technique was applied to convert text into numeric as feature extraction. This study has implemented a Random Forest classifier to predict real and fake news. The proposed model contributes to overall classification precision by comparing it to the existing models. This fake news detection highlights the efficacy of the TF-IDF vectorizer and Random Forest combination which achieved an impressive accuracy rate of 99.0%. This contribution highlights an effective strategy for combating misinformation through precise text classification.
Deteksi Ekspresi Wajah Pada Scene Film Menggunakan Residual Masking Network Bhanu Sri Nugraha; Fahma Inti Ilmawati; Dhani Ariatmanto; Lukman
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i2.4422

Abstract

In a film, there are various things that must be paid attention to, one of which is the actor's expression in deepening his role. This is what can make the audience immersed in the storyline of the film and can provide value for the accuracy of deepening the role for the people who watch it. With the popularity of deep learning, especially CNN (Convolutional Neural Network) can automatically extract and learn for a good facial expression recognition system. In this experiment, we use Residual Masking Network (RNM). Building on this understanding, we evaluate this dataset with standard image classification models to analyse the feasibility of using facial expressions in determining the appropriateness of emotional content in an actor's role in a film. The accuracy results in this study were 99% for detecting angry expressions.
Analisis Akurasi Deteksi Objek Menggunakan TensorFlow dengan Metode Single Shot Detection untuk Pengenalan Karakter Animasi dalam Film Battle of Surabaya Triesa Wea, Engel Bertus; Ariatmanto, Dhani
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 7 (2025): JPTI - Juli 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.905

Abstract

Pendeteksian karakter dalam film animasi secara otomatis masih menghadapi tantangan signifikan, seperti kompleksitas latar belakang, variasi pencahayaan, dan perbedaan pose karakter, yang dapat menurunkan akurasi sistem. Penelitian ini bertujuan untuk meningkatkan akurasi pendeteksian karakter animasi dengan menerapkan metode Single Shot Detection (SSD) menggunakan TensorFlow. Studi kasus difokuskan pada karakter dalam film animasi Battle of Surabaya. Metode SSD dipilih karena kemampuannya dalam melakukan deteksi objek secara cepat dan efisien dalam satu tahap pemrosesan. Dataset yang digunakan terdiri dari 30 gambar karakter Musa, Yumna, dan Danu dalam berbagai pose dan kondisi visual. Proses pelatihan model dilakukan menggunakan pendekatan transfer learning dan augmentasi data untuk meningkatkan keragaman data latih serta mengurangi risiko overfitting. Hasil pengujian menunjukkan bahwa model mencapai akurasi rata-rata sebesar 99% pada dataset uji, dengan performa yang tetap stabil meskipun terdapat variasi latar belakang dan pencahayaan. Analisis lebih lanjut juga dilakukan terhadap faktor-faktor yang memengaruhi kinerja model. Penelitian ini berkontribusi dalam pengembangan sistem deteksi karakter animasi yang akurat dan efisien, dengan potensi penerapan dalam industri animasi, sistem pengawasan visual, aplikasi edukatif interaktif, dan pengembangan teknologi computer vision. Selain itu, sistem ini dapat mendukung identifikasi karakter secara otomatis untuk tujuan perlindungan hak cipta visual pada media digital.
PENINGKATAN RESOLUSI VIDEO MUSIK LAWAS INDONESIA MENGGUNAKAN REAL ESRGAN X4PLUS Alfansani, Abdul Rauf; Utami, Ema; Ariatmanto, Dhani
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 7 No 2 (2025): EDISI 24
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v7i2.6146

Abstract

Video musik Indonesia lawas yang diproduksi pada era VCD umumnya memiliki kualitas visual rendah dan degradasi artefak yang signifikan. Penelitian ini mengevaluasi efektivitas model Real?ESRGAN?X4plus dalam meningkatkan resolusi video-video tersebut menggunakan pendekatan super?resolusi berbasis pembelajaran mendalam. Eksperimen dilakukan pada lima video dengan resolusi awal 480p yang ditingkatkan menjadi 1920p, dengan variasi parameter denoise_strength dari 0,0 hingga 1,0 dan tile_size dari 64 hingga 256. Model ini dibandingkan dengan metode interpolasi bicubic dan ESRGAN publik sebagai baseline. Evaluasi dilakukan menggunakan metrik PSNR dan SSIM, serta uji statistik paired t?test. Hasil menunjukkan bahwa konfigurasi optimal adalah denoise_strength = 0.0 dan tile_size = 256, menghasilkan PSNR tertinggi (35,10 dB) dan SSIM terbaik (0,956). Dibanding baseline, Real?ESRGAN memberikan peningkatan kualitas yang signifikan baik secara obyektif maupun visual. Model ini juga menunjukkan potensi dalam mempertahankan detail halus dan konsistensi temporal antar frame. Temuan ini mendukung pemanfaatan Real?ESRGAN sebagai solusi restorasi video budaya Indonesia, serta membuka peluang untuk pengembangan lanjut berbasis VSR temporal.
Comparative Analysis of Live Action Film Production Management Using Critical Path Method (CPM) Versus Conventional Production Processes Nugroho, Agung; Suyanto, Mohammad; Ariatmanto, Dhani
Intechno Journal : Information Technology Journal Vol. 7 No. 1 (2025): July
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2025v7i1.2019

Abstract

The production of the film “Kinah dan Redjo”, by Universitas Amikom and MSV Sinema, has been completed, prompting researchers to conduct an analysis and evaluation of the production management applied. The focus of this study is on time and cost, which are critical factors supporting film production. An extended production duration was identified as a challenge, as it reduces effectiveness and leads to cost overruns. Therefore, this study aims to compare project management strategies for successful planning and control, using both conventional methods and the Critical Path Method (CPM). This analysis is expected to yield faster project completion and establish efficient, productive standards for future productions. The conventional approach indicated a total production duration of 681 days, comprising 120 days for pre-production, 18 days for production, and 551 days for post-production. Upon analysis using the CPM method, the total duration was reduced to 459 days, including 113 days for pre-production, 152 days for production, and 191 days for post-production. The graphical comparison of methods shows significant cost fluctuations across each production phase with the conventional method, especially increased costs during production despite the shorter duration. Conversely, the CPM method demonstrates more controlled and measurable durations and costs. This study underscores the importance of cost optimization, standardization of the Work Breakdown Structure (WBS), and hybrid modeling to enhance efficiency in dynamic film projects. Furthermore, this analysis serves as a foundational reference for the architectural planning of future applications incorporating artificial intelligence (AI) integration. AI has the potential to accelerate scheduling, optimize resource allocation, and streamline cost management and production design, thereby improving overall project efficiency.
Hybrid Fuzzy Logic, Genetic Algorithms, and Artificial Neural Networks for Cattle Body Weight Prediction Anjar Setiawan; Ema Utami; Dhani Ariatmanto
Jurnal ilmiah Sistem Informasi dan Ilmu Komputer Vol. 5 No. 2 (2025): Juli : Jurnal ilmiah Sistem Informasi dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juisik.v5i2.1319

Abstract

Cattle serve as the primary means of meat and milk production in numerous regions across the globe. Enhancing efficiency and productivity in cow ranching can provide significant economic consequences. The cattle industry is significant as it enables the estimation of cow weight, directly influencing beef and milk quality. This study aims to enhance the accuracy of cattle weight estimation by minimizing the Mean Squared Error (MSE) values. The integration of artificial neural network (ANN), fuzzy logic (FL), and genetic algorithm (GA) techniques is a promising artificial intelligence tool for predicting and modeling cattle weight in livestock weight prediction systems. The cow weight forecast yielded a Mean Squared Error (MSE) value of 10.9 kg, which is the best result. The results demonstrate the progress made in agriculture using advanced technologies. They offer a detailed examination of how artificial intelligence, fuzzy logic, and evolutionary techniques can be combined to address the many difficulties associated with estimating cattle body weight.
ANALYSIS OF DIGITAL IMAGE RECOGNITION OF INDONESIAN SIGN LANGUAGE USING THE DEEP LEARNING CNN ARCHITECTURE VGG19 METHOD Prayoga, Dimas; Utami, Ema; Ariatmanto, Dhani
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 3 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i3.7353

Abstract

This study examines the application of the CNN method with the VGG19 architecture for digital image analysis in recognizing Indonesian sign language. The data used in this study is the BISINDO data set type, with 8,814 samples divided into 26 alphabetical categories. Implementing sign language recognition using the VGG19 architecture produces good accuracy results, reaching 93.24% with epoch 25 (without hyper-parameters tuning).These results confirm the model's extraordinary ability in image recognition and performing precise analysis. However, the results of this study can be improved again by performing Hyper parameters tuning on the architecture used, namely VGG19, by changing certain variables that affect increasing accuracy. Other aspects can be improved to achieve optimal performance, considering the excellent results. By integrating modern hyper-parameter tuning approaches and incorporating a variety of additional data, the model generalization is expected to be improved, leading to higher accuracy in many real-world settings
Deteksi Karakter Aksara Jawa Menggunakan YOLO11 Pendekatan Deep Learning untuk Pelestarian Warisan Budaya Digital Darmawan, Eko Rahmad; Ariatmanto, Dhani
Jurnal Bangkit Indonesia Vol 14 No 2 (2025): Bulan Oktober 2025
Publisher : LPPM Sekolah Tinggi Teknologi Indonesia Tanjung Pinang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52771/bangkitindonesia.v14i2.450

Abstract

Javanese script represents a significant cultural heritage of the Indonesian archipelago that faces extinction threats due to Latin alphabet dominance and minimal users capable of writing with this traditional script. This research aims to develop a Javanese character detection system using You Only Look Once version 11 (YOLO11) algorithm to support cultural preservation efforts through efficient digitalization. The research methodology employs an experimental approach with deep learning, where the Javanese script dataset consisting of 20 basic characters plus background class was obtained from Kaggle and preprocessed using Roboflow with data augmentation techniques. The YOLO11 model was implemented with SGD optimizer, 640px image size, and trained for 500 epochs to achieve optimal convergence. YOLO11 architecture integrates advanced components such as C3K2 blocks, Spatial Pyramid Pooling-Fast (SPPF), and Cross-scale Pixel Spatial Attention (C2PSA) to enhance multiscale feature extraction capabilities. Model performance evaluation utilized confusion matrix with accuracy, precision, recall, and F1-score metrics. Research results demonstrate that the YOLO11 model achieved an overall accuracy of 81.00% with macro-averaged precision of 86.28%, macro-averaged recall of 87.25%, and macro-averaged F1-score of 86.41%. Model performance distribution shows 7 classes with high performance (F1-score ≥ 90%), 9 classes with medium performance (80-90%), and 4 classes with low performance (<80%). The "nga" class achieved perfect performance of 100%, while the "ha" class showed the lowest performance with an F1-score of 68.09%. This research successfully improved accuracy compared to previous methods using backpropagation neural networks (74%) and conventional backpropagation (59.5%), although challenges remain in detecting characters with similar shapes and handling background class. The main contribution is the first implementation of YOLO11 for Javanese script detection, opening opportunities for developing more efficient and accurate ancient literature digitalization systems.
Perbandingan Metode Ekstraksi Fitur LBP, GLCM, dan Canny dalam Klasifikasi Penyakit Daun Padi dengan KNN Jordy, Roy; Ariatmanto, Dhani
Jurnal Bangkit Indonesia Vol 14 No 2 (2025): Bulan Oktober 2025
Publisher : LPPM Sekolah Tinggi Teknologi Indonesia Tanjung Pinang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52771/bangkitindonesia.v14i2.452

Abstract

Accurate and timely identification of rice leaf diseases plays a crucial role in supporting early disease control efforts in agriculture. This study aims to compare the performance of three image feature extraction methods—Local Binary Pattern (LBP), Gray Level Co-occurrence Matrix (GLCM), and Canny Edge Detection—in classifying three types of rice leaf diseases: Bacterial leaf blight, Brown spot, and Leaf smut. Each method was evaluated based on its confusion matrix as well as key performance metrics, including accuracy, precision, recall, and F1-score. Experimental results show that LBP achieved the highest classification performance with an accuracy of 92.06%, followed by GLCM at 78.57% and Canny at 66.67%. In addition to accuracy, LBP also outperformed the other methods across all evaluation metrics. These findings indicate that the local texture features captured by LBP are more effective in distinguishing disease types compared to the global texture features from GLCM and edge-based features from Canny. Therefore, LBP is recommended as a superior feature extraction method for automated classification systems of rice leaf diseases based on digital imagery.
Co-Authors Adrianto M. Wijaya Afifah Nur Aini Agung Nurhidayatullah, Rizqy Ahsan, Muhammad Salimy Alfansani, Abdul Rauf Alva Hendi Muhammad Amali, Amali Anggriandi, Dendi Anjar Setiawan Anton, Tri Arfian Hendro Priyono Arfian Yogi Ferianto Arief Setyanto Aris Setiyadi Bangun Watono BHANU SRI NUGRAHA Chaedar Fatach, Muhamad Reza Darmawan, Eko Rahmad Didi Kurniawan Edy Widodo Ema Utami Eva Putriany Fadilla, Rizqi Mirza Fahma Inti Ilmawati Firdaus, M. Haikal Ghifari, Dloifur Rohman Al Hadiguna Setiawan Hayaty, Mardhiya Ibnu Hadi Purwanto Jordy, Roy Karim, Hildamayanti Khairani, Fatihatul KUSRINI Kusrini Kusrini Kusrini, Kusrini Linggama, Muhamad Nurfaizi Lukman Lukman M. Ilham Arief M. Shahkhir Mozamir M. Ulil Albab Majid, Muhammad Arif Kholis Metha, Halifa Sekar Mohammad Suyanto Muhammad Rifa'i, Anggi Muhammad Salimy Ahsan Mulia Sulistiyono Napitupulu, Harapan Norhikmah . Nugroho, Agung Nurrizky, Akbar Nur’aini Pramudyantoro, Arvi Prasetiyanto, Ari Eka Prayoga, Dimas Putriany, Eva Quratul Ain Ramdhani, Mohamad Dhicy Rifai, Anggi Muhammad Rosmantyo, Windhy Rokhmat Royali, Rainer Maghma Rusdi Rahman, Muhammad Selvi Marcellia Silvi Agustanti Bambang Slameto, Andika Agus Subandono, Anelta Tirta Putri Sukoco Sukoco Supriadi, Oki Akbar Suryono, Wachid Daga Suyanto, M Tikasni, Elisa Triesa Wea, Engel Bertus Turmudi Zy, Ahmad Wijaya, Adrianto Mahendra Wiwik Handayani Zakaria Zakaria zakinah, annisa gatri Zuhri, Muhammad Rafli Zulpan Hadi