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All Journal Jurnal Sarjana Teknik Informatika Jurnal Transformatika Jurnal Informatika dan Teknik Elektro Terapan InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Jurnal Teknologi dan Sistem Komputer Jurnal Pengabdian UntukMu NegeRI JIKO (Jurnal Informatika dan Komputer) Insect (Informatics and Security) : Jurnal Teknik Informatika Indonesian Journal of Information System Dinamisia: Jurnal Pengabdian Kepada Masyarakat SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) Compiler Jurnal Teknologi Sistem Informasi dan Aplikasi JUTEKIN (Jurnal Manajemen Informatika) INTEK: Informatika dan Teknologi Informasi AMALIAH: JURNAL PENGABDIAN KEPADA MASYARAKAT Antivirus : Jurnal Ilmiah Teknik Informatika Progresif: Jurnal Ilmiah Komputer Jurnal Ilmu Komputer dan Bisnis Jurnal Manajemen Informatika dan Sistem Informasi JATI (Jurnal Mahasiswa Teknik Informatika) JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Journal of Computer System and Informatics (JoSYC) International Journal of Advances in Data and Information Systems Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Restikom : Riset Teknik Informatika dan Komputer Mosharafa: Jurnal Pendidikan Matematika Journal of Informatics Management and Information Technology Prosiding Seminar Nasional Program Pengabdian Masyarakat sudo Jurnal Teknik Informatika Konstelasi: Konvergensi Teknologi dan Sistem Informasi Jurnal Teknik Informatika Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer Jurnal Informatika Teknologi dan Sains (Jinteks) Industrial Research Workshop and National Seminar Jurnal Sains Informatika Terapan (JSIT) Journal Of Information System And Artificial Intelligence Jikom: Jurnal Informatika dan Komputer J-Icon : Jurnal Komputer dan Informatika SENAPAS BIMASAKTI
Mutaqin Akbar
Universitas Mercu Buana Yogyakarta
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IMPLEMENTASI SISTEM CHARACTER PLAYER PADA GAME RPG 2D MENGGUNAKAN GAME ENGINE GODOT Kartadinata, Arifqi; Akbar, Mutaqin
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 3 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i3.7163

Abstract

Perkembangan industri game mendorong kebutuhan akan sistem kendali karakter dan pertarungan yang responsif dalam genre Action RPG 2D. Penelitian ini bertujuan untuk mengimplementasikan sistem character player yang mencakup character controller dan combat system menggunakan Godot Engine. Character controller dirancang untuk memberikan kendali yang halus terhadap pergerakan karakter seperti bergerak, berlari, dan melompat, sedangkan combat system difokuskan pada mekanisme attack, deteksi musuh, upgrade stats, dan pengurangan damage musuh. Metode yang digunakan adalah rekayasa perangkat lunak dengan tahapan analisis, perancangan, implementasi, dan pengujian. Sistem kendali karakter dikembangkan menggunakan Hierarchical Finite State Machine (HFSM) agar modular dan mudah dikembangkan, sedangkan sistem pertarungan memanfaatkan area dan animasi yang sinkron. Hasil pengujian menunjukkan bahwa sistem berjalan dengan baik dan memberikan pengalaman bermain yang responsif dan stabil. Implementasi ini penting sebagai acuan pengembangan game Action RPG 2D berbasis open-source.
KLASIFIKASI CITRA BIJI KOPI TEMANGGUNG MENGGUNAKAN GRAY LEVEL CO-OCURRENCE MATRIX – CONVOLUTIONAL NEURAL NETWORK Muhammad Syadham, Syahrun; Akbar, Mutaqin
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 3 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i3.7274

Abstract

Kabupaten Temanggung dikenal sebagai daerah penghasil kopi unggulan di Jawa Tengah. Namun, identifikasi varietas biji kopi seperti Arabika, Robusta, dan Excelsa masih banyak dilakukan secara visual yang rawan kesalahan karena sifatnya yang subjektif. Penelitian ini bertujuan merancang sistem klasifikasi citra biji kopi Temanggung dengan mengombinasikan metode Gray Level Co-occurrence Matrix (GLCM) dan Convolutional Neural Network (CNN). Sebanyak 1.350 citra biji kopi diolah dan diekstraksi menjadi 16 fitur tekstur menggunakan GLCM dari empat arah orientasi. Fitur tersebut digunakan sebagai input untuk arsitektur CNN berlapis 16. Dataset dibagi menjadi 80% untuk pelatihan dan 20% untuk pengujian. Hasil pelatihan menunjukkan akurasi sebesar 96,67%, sedangkan pada pengujian mencapai 96,30%. Temuan ini membuktikan bahwa kombinasi GLCM dan CNN mampu menghasilkan akurasi yang baik walaupun masih belum bisa mengungguli penelitian sebelumnya.
Sistem Pendukung Keputusan untuk Penentuan Investasi Cryptocurrency Menggunakan Metode Weighted Product Saputra, Aldi Dwi; Akbar, Mutaqin
Journal of Informatics Management and Information Technology Vol. 5 No. 3 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jimat.v5i3.570

Abstract

The rapid growth of cryptocurrencies with diverse characteristics poses challenges for investors, especially beginners, in selecting optimal investment options. This study seeks to construct and develop a decision support system (DSS) utilizing the Weighted Product (WP) technique to deliver objective recommendations for cryptocurrency investments. The evaluation criteria were determined through questionnaires distributed to investors, identifying five main criteria: asset liquidity, daily transaction volume, market capitalization, price volatility, and current cryptocurrency price. The system was developed using PHP, CSS, and JavaScript with Visual Studio Code as the development environment. The calculation results indicate that Bitcoin ranks first with a score of 0.058263, followed by Ethereum (0.05240), and Solana (0.044023). The developed system effectively generates cryptocurrency asset rankings by considering weights and values for each criterion, assisting users in making more accurate and optimal investment decisions.
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN BAND TERBAIK MENGGUNAKAN ANALYTICAL HIERARCHY PROCESS DAN MULTI-FACTOR EVALUATION PROCESS Arifadillah, Elang; Akbar, Mutaqin
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.v7i2.5856

Abstract

Penilaian terhadap peserta festival musik sering kali bersifat subjektif dan kurang transparan, sehingga diperlukan sistem pendukung keputusan yang mampu memberikan hasil evaluasi secara objektif dan terstruktur. Penelitian ini menyajikan sistem pendukung pemilihan band terbaik menggunakan Analytical Hierarchy Process (AHP) dan Multi-Factor Evaluation Process (MFEP). Tujuan dari penelitian ini untuk merancang dan mengimplementasikan sistem pendukung keputusan dalam pemilihan band terbaik pada ajang Mentalita Fest dengan mengintegrasikan dua metode pengambilan keputusan multikriteria, yaitu AHP dan MFEP. Metode AHP digunakan untuk menentukan bobot dari setiap kriteria penilaian berdasarkan preferensi juri melalui matriks perbandingan berpasangan, sedangkan metode MFEP digunakan untuk menghitung skor akhir dari masing-masing alternatif berdasarkan bobot yang telah diperoleh. Tiga kriteria utama yang digunakan adalah teknik bermusik, performa panggung, dan orisinalitas musik. Berdasarkan hasil evaluasi terhadap 15 band peserta, diperoleh bahwa SUPERMAN IS DEAD merupakan band terbaik dengan nilai total tertinggi sebesar 4.30. Sistem juga menyediakan visualisasi hasil akhir dalam bentuk peringkat dan rekomendasi keputusan. Dengan demikian, sistem ini dapat dijadikan solusi evaluasi yang transparan dan aplikatif dalam berbagai kegiatan seleksi berbasis kriteria ganda, khususnya dalam bidang seni pertunjukan.
Multi-Task Learning for Traffic Sign Recognition using Multi-Scale Convolutional Neural Networks Akbar, Mutaqin; Susilawati, Indah; Jati, Budi Sulistiyo; Alamsyah, Nur
International Journal of Advances in Data and Information Systems Vol. 6 No. 2 (2025): August 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i2.1406

Abstract

Traffic signs are an essential component of road infrastructure. According to the Department of Transportation, Indonesia has over 300 distinct traffic signs, categorized based on their functions and purposes. TSR systems have been widely integrated into various intelligent transportation technologies, such as Driver Assistance Systems (DAS), Advanced Driver Assistance Systems (ADAS), and Autonomous Driving Systems (ADS). The output generated by TSR serves as a critical input for DAS, ADAS, ADS, and other intelligent systems. This article presents a CNN-based classification for traffic sign recognition using multi-task learning (MTL), focusing on traffic signs in Indonesia. The dataset was collected from direct capture with the help of a cellphone camera, indirect capture by utilizing screenshots on a digital map application, and they are captured from several different angles, during the day and at night. The proposed CNN architecture incorporates multi-scale within an MTL framework. The use of a multi-scale approach will hopefully enhance the model’s ability to recognize traffic signs in varied and complex environments. And the integration of MTL will enable the model to handle multiple related tasks concurrently, sharing learned features across tasks. During the training stage, the MS-CNN outperformed a standard CNN model by demonstrating lower initial loss, higher starting accuracy, and achieving 100% accuracy by the 8th epoch with a minimal error rate of just 0.003. In the testing stage, the model achieved exceptional results, as shown by the confusion matrix, it successfully classified all traffic sign types (10 classes) and accurately categorized each sign into one of two categories—warning or prohibition. All performance metrics, including precision, recall, and F1-score, reached 100% for both output tasks, confirming the robustness and reliability of the model.
Sistem Penunjang Keputusan Pemilihan Pakan Kering Anak Kucing Menggunakan Simple Additive Weighting Sedyarsa, Hanif Fauzan; Akbar, Mutaqin
Insect (Informatics and Security): Jurnal Teknik Informatika Vol. 11 No. 2 (2025): Oktober 2025
Publisher : Universitas Muhammadiyah Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33506/insect.v11i2.4772

Abstract

This study aims to develop a decision support system for selecting dry cat food for kittens using the Simple Additive Weighting (SAW) method. The system is designed to assist cat owners in choosing available food products at Galaxy Petshop based on several important criteria, namely price, protein content, carbohydrates, quality of raw materials, age suitability, and moisture content. The decision-making process involves normalizing the data, assigning weights to each criterion, and calculating preference values to rank the alternative cat foods. The system is implemented using the Python programming language and tested with six product alternatives. The results indicate that the system is capable of providing the best alternative recommendation based on the criteria, with Proplan Kitten as the top alternative with a preference value of 0.755, followed by Excel Kitten (0.660) and Royal Canin Kitten (0.630). This system has proven effective in helping users determine the most suitable cat food according to nutritional needs and budget, and it has potential for further development by adding more criteria and integrating with digital platforms.
KLASIFIKASI CITRA SINTETIS HASIL MODEL DIFUSI MENGGUNAKAN GRAY LEVEL CO-OCCURRENCE MATRIX (GLCM) dan CONVOLUTIONAL NEURAL NETWORK (CNN) Hardiyanto, Andri; Akbar, Mutaqin
JURNAL INFORMATIKA DAN KOMPUTER Vol 9, No 3 (2025): Oktober 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiko.v9i3.2033

Abstract

Kemajuan teknologi kecerdasan buatan khususnya dalam bidang pengolahan citra telah memungkinkan penciptaan gambar buatan yang sangat menyerupai gambar nyata, sehingga menimbulkan tantangan dalam verifikasi keaslian citra digital. Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi citra untuk membedakan antara citra asli dan citra hasil kecerdasan buatan dengan pendekatan hybrid Gray Level Co-occurrence Matrix (GLCM) untuk mengekstraksi enam fitur tekstur dari citra grayscale dengan fitur spasial dari Convolutional Neural Network (CNN) yang kemudian digabungkan untuk membentuk vektor fitur gabungan. Dataset yang digunakan terdiri dari 3.410 citra berwarna yang terbagi secara seimbang ke dalam dua kelas real dan fake. Hasil pengujian CNN murni mencapai akurasi 97%, dengan presisi dan recall antara 0.95-0.99,serta f1-score 0.97. Sementara itu, pada model GLCM-CNN akurasinya mencapai 98% dengan nilai presisi dan recall 0.96-1.00 serta f1-score 0.98. Integrasi fitur tekstur dari GLCM terbukti mampu meningkatkan sensitivitas model terhadap pola mikro pada citra buatan yang tidak dapat ditangkap oleh CNN. Penelitian ini menunjukkan potensi pendekatan hybrid sebagai dasar pengembangan sistem pendeteksi citra sintetis yang adaptif dan akurat di masa mendatang.
Analisis Sentimen Jogja Darurat Sampah di Twitter menggunakan Ekstraksi Fitur Model Word2Vec dan Convolutional Neural Network Yusanto, Yoga; Akbar, Mutaqin
TIN: Terapan Informatika Nusantara Vol 4 No 10 (2024): March 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v4i10.4952

Abstract

Due to a waste emergency, the Special Region of Yogyakarta has garnered public attention and sparked discussions. Numerous community groups express their opinions through various social media platforms, especially Twitter. It's undeniable that Twitter is currently one of the places for freely expressing opinions. Therefore, sentiment analysis plays a crucial role in efforts to categorize public opinions on something trending or viral into three categories: positive, negative, and neutral. In this study, the dataset was obtained using scraping techniques and the tweetscraper tool from the APIFY actor web.harvester/easy-twitter-search-scraper. The method employed in this analysis is the Convolutional Neural Network (CNN) classification method using Word2Vec model extraction. The study involves 505 tweets in Bahasa Indonesia with the hashtags #JogjaDaruratSampah (#JogjaDaruratSampah) and #TPSTPiyungan as data. Out of these, 381 tweets are utilized as training data, and the remaining 124 tweets are used as test data. The highest accuracy in testing the training data was achieved in the 19th epoch with a 90% accuracy rate. It can be concluded from the testing process that this study can identify positive, negative, and neutral sentiments with an accuracy of 53%. The sentiment analysis results indicate a significant amount of negative tweets, accounting for 49.7% of the total 505 tweets.
Klasifikasi Dialek Bahasa Inggris British dan Amerika menggunakan Support Vector Machine Kuswandaru, Kuswandaru; Akbar, Mutaqin
TIN: Terapan Informatika Nusantara Vol 4 No 10 (2024): March 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v4i10.4965

Abstract

English has become an international language used in various fields, including education, business, and tourism. Indonesia, having become a member of the AEC (Asean Economic Community), makes it increasingly important for Indonesian society, especially the younger generation, to master English proficiently and accurately. English, as an international language, encompasses numerous dialects, such as British and American dialects. This research is motivated by the issue that differences between British and American English dialects can affect understanding and communication in educational, business, and everyday life contexts. Identifying and classifying dialects in English speech is crucial to aid both native and non-native speakers in better understanding communication contexts. This study aims to develop a classification method using the Support Vector Machine (SVM) algorithm to distinguish between British and American English dialects in speech. By leveraging SVM, this research will attempt to identify linguistic features that differentiate between these dialects, such as intonation, vowels, consonants, and rhythm patterns obtained from sound feature extraction using Mel Frequency Cepstral Coefficients (MFCC). In this model training phase, a dataset comprising 720 speech samples collected from various text-to-speech service provider websites is utilized to represent both dialects. Subsequently, the trained model is tested using 24 test data collected from original recordings of several individuals to evaluate its accuracy. The results of this research yield an accuracy rate of 91.6% on the model with a configuration of Cost value 1, gamma 0.001, and polynomial kernel. From these results, it can be concluded that this model exhibits a sufficiently high accuracy, with 2 misclassifications out of 24 test data.
Feasibility of Opportunity Material Module with Joymath Cognitive Behavioral Method to Reduce Mathematics Anxiety and Increase Student Self-Efficacy Marhaeni, Nafida Hetty; Khuzaini, Nanang; Rizky, Muhammad Rafi Fajar; Yuniasanti, Reny; Akbar, Mutaqin; Sari, Dian Kartika; Dangin, Dangin
Mosharafa: Jurnal Pendidikan Matematika Vol. 13 No. 3 (2024): July
Publisher : Department of Mathematics Education Program IPI Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31980/mosharafa.v13i3.2188

Abstract

Siswa memerlukan media pembelajaran yang dapat membantu siswa mengurangi kecemasan matematika dan meningkatkan efikasi diri. Tujuan penelitian yaitu mengembangkan modul untuk mengurangi kecemasan matematika dan meningkatkan efikasi diri siswa pada materi peluang. Penelitian Research and Development ini menggunakan model ADDIE dengan lima tahap pengembangan yaitu analisis, desain, pengembangan, implementasi, dan evaluasi. Sumber data dan subjek dalam penelitian ini adalah ahli materi, ahli media, guru, dan 1328 siswa dari 24 SMP di Kota Yogyakarta. Teknik pengumpulan data yang digunakan yaitu wawancara, observasi, Focus Group Discussion (FGD), dan angket. Hasil menunjukkan bahwa produk yang dikembangkan yaitu modul materi peluang untuk mengurangi kecemasan matematis dan meningkatkan efikasi diri siswa layak digunakan dalam pembelajaran matematika. Pengambilan keputusan kelayakan produk didasarkan pada hasil analisis penilaian dari validator, ahli materi, dan ahli media yang menunjukkan bahwa modul sangat valid dan berpeluang menurunkan kecemasan matematis dan meningkatkan efikasi diri siswa.Students need learning media that can help students reduce mathematics anxiety and increase self-efficacy. The aim of the research is to develop a module to reduce mathematics anxiety and increase students' self-efficacy in opportunity material. This Research and Development research uses the ADDIE model with five development stages, namely analysis, design, development, implementation and evaluation. The data sources and subjects in this research were material experts, media experts, teachers, and 1328 students from 24 junior high schools in Yogyakarta City. The data collection techniques used were interviews, observation, Focus Group Discussion (FGD), and questionnaires. The results show that the product developed, namely the opportunity material module to reduce mathematical anxiety and increase students' self-efficacy, is suitable for use in mathematics learning. Decision making on product feasibility is based on the results of assessment analysis from validators, material experts and media experts which show that the module is very valid and has the opportunity to reduce mathematical anxiety and increase student self-efficacy.
Co-Authors Adella Maharani, Putri Agung Firmansyah Agus Salim Ahsan, Moh An-Naufal Nuha, Alfian Anisyah Jatu Siti Nurjanah Aprisia Bahagia, Grace Arifadillah, Elang Arita Witanti Ascha, Nugrah Pratama Astri Wulandari Audita Nuvriasari Auditya, Yonathan Bagus Dwi Kurniawan, Bagus Dwi Bambang Agus Setyawan Budi Sulistiyo Jati Budianto, Alexius Endy Dangin, Dangin Dian Kartika Sari, Dian Kartika Diski Ijtima Putri Dwiyati Pujimulyani Elsa Anggraini Maili Ertandi, Fiki Febri Rahmadsyah Firdaus Alfajar Sudarsih Hardiyanto, Andri Hendri Tri Cahya Leksana Ichlasia Ainul Fitri Ikram, Rauf Al Indah Susilawati Jeremias Quintino Tilman Junianto Bagas Prasetyo Kafilahudin, Fahrul Advis Kartadinata, Arifqi Khuzaini*, Nanang Kuswandaru, Kuswandaru Marfianto, Jodhy Dwi Muhammad Abdul Gofur Muhammad Ali Ma'mun Muhammad Pratiwo Muhammad Syadham, Syahrun Muhammad Syaifudin Musa, Rahmat Nafida Hetty Marhaeni Nanang Khuzaini, Nanang Nanik Triatmi Nur Alamsyah Nurdiarti, Rosalia Prismarini Nusantara, Bondan Surya Pascal Munthe, Thimoty Prasetyaningrum, Putri Taqwa Primananda, Muhammad Izra Priyanto Putu Sangyoga, Titus Bintang Pekiek Rahmat Musa Ramos, Sarah Vega Refky Satria Bima Reny Yuniasanti Rio Setya Pambudi Rismanto, Septa Rivansyah Subagyo, Ibnu Riyanto, Agung Rizky, Muhammad Rafi Fajar Rofiqi, Lutfi Rohmad, Arinadi Nur Rosalia Prismarini Nurdiarti Saputra, Aldi Dwi Saputra, Andika Dwi Sari, Prima Wulan Sedyarsa, Hanif Fauzan Septa Rismanto Setyaningsih, Putry Wahyu Sidiq Purnomo, Agus Sri Muhammad Kusumantomo Subhan Bole Boly Supatman Supatman Umul Aiman Wakidi Wakidi Wibowo, Sigit Heri Wisnu Adi Yulianto Wulandari, Astri Yusanto, Yoga