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All Journal MANAJEMEN IKM: Jurnal Manajemen Pengembangan Industri Kecil Menengah Jurnal Informatika Jurnal Algoritma Neo-Bis Jurnal Ilmiah KOMPUTASI RABIT: Jurnal Teknologi dan Sistem Informasi Univrab ILKOM Jurnal Ilmiah SISFO Jurnal RESISTOR (Rekayasa Sistem Komputer) Jurnal Ilmiah Media Sisfo Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Teknologi Sistem Informasi Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI) Kharisma Tech Jurnal Algoritma Journal of Technology Research in Information System and Engineering
Mohammad Fajar, Mohammad
STMIK Kharisma Makassar
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering

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PEMANFAATAN ARTIFICIAL INTELLIGENCE SEBAGAI ALAT BANTU PENGUJIAN APLIKASI VISUAL NOVEL MYSTIC PAGES MENGGUNAKAN METODE BLACK-BOX Elisya Belia Maria Engka; Mohammad Fajar; Baizul Zaman
KHARISMA Tech Vol 21 No 1 (2026): KHARISMATech Journal
Publisher : STMIK KHARISMA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55645/kharismatech.v21i1.602

Abstract

Application or software testing is one of the important stages in application development, as it ensures that the application meets the defined specifications or user requirements. This also applies to visual novel applications, which face challenges in the testing process, such as ensuring that the narrative runs smoothly and logically, and that the features work without any obstacles. This study aims to utilize Artificial Intelligence (AI) as a testing tool for the visual novel application Mystic Pages. The AI application Gemini is used to assist in the automatic generation of testing scenarios using equivalence partitioning and boundary value analysis techniques. Data collection was conducted through literature review, AI prompt testing, and the designed application. Evaluation showed that the Gemini AI used could generate structured, systematic, and easy-to-use test scenario outputs for equivalence partitioning and boundary value analysis techniques. If done manually without using Gemini AI, scenario descriptions took approximately 1 hour and 20 minutes to generate. Meanwhile, using Gemini AI only takes approximately 10 minutes. The Gemini AI prompt entered must include the Mystic Page application specification table and a request for scenario creation based on the desired technique. Additionally, the test scenarios generated by Gemini AI can be used to test the Mystic Page application.
DEVELOPMENT OF A LONTARA CHARACTER DETECTION APPLICATION BASED ON CONVOLUTIONAL NEURAL NETWORKS USING THE FLUTTER FRAMEWORK Linda Yanti Yo; Mohammad Fajar; Baizul Zaman
KHARISMA Tech Vol 21 No 2 (2026): KHARISMATech Journal
Publisher : STMIK KHARISMA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55645/kharismatech.v21i2.603

Abstract

Lontara script is a cultural heritage of the Bugis-Makassar people that is increasingly threatened with extinction due to the limited availability of interactive learning media. This study aims to develop a mobile application for detecting Lontara script based on Flutter, integrated with a Convolutional Neural Network (CNN) model. The dataset consists of 5,017 images of Lontara script divided into 23 classes, processed through grayscale and normalization stages. The CNN model is designed with three layers of convolution and pooling, then evaluated using accuracy, precision, recall, and F1-score metrics. Testing results show that the application can detect Lontara script in real-time through Flutter and REST API integration, with the model achieving 97% accuracy. Testing under conditions with visual disturbances showed that the model could still perform detection up to a certain level of noise and blur, but performance declined under high simultaneous disturbances. These findings confirm that the application can support efforts to preserve Lontara script, though it still requires adequate image quality for optimal detection.
Identifikasi Barang Secara Otomatis Pada E-Thrift Warshop Menggunakan Deep Learning Christofer Reynaldy Wijaya; Mohammad Fajar; Baizul Zaman
JTRISTE Vol 13 No 2 (2026): JTRISTE
Publisher : STMIK KHARISMA Makassar

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

Abstract

Penelitian ini bertujuan untuk mengimplementasikan teknologi deep learning dalam pengenalan barang pada aplikasi e-thrift Warshop, yang merupakan platform jual beli barang bekas. Masalah utama yang dihadapi adalah identifikasi barang yang sering kali subjektif dan tidak akurat dalam proses manual. Dengan menggunakan teknologi machine learning berbasis Convolutional Neural Networks (CNN) dan MobileNet V1, penelitian ini mencoba mengatasi tantangan tersebut. Metode yang digunakan meliputi pengunggahan gambar oleh pengguna, pemrosesan gambar oleh model MobileNet yang di-host di jsDelivr, dan penyimpanan hasil klasifikasi di Airtable untuk ditampilkan di situs web Warshop. Hasil penelitian menunjukkan bahwa model yang diterapkan berhasil mencapai akurasi sebesar 75.5%, presisi 100%, dan recall 77.5%.Kesimpulan dari penelitian ini adalah bahwa teknologi deep learning dapat meningkatkan keakuratan dalam pengenalan barang, meskipun ada kebutuhan untukmemperluas dataset dan memperbarui model secara berkala. Fitur feedback dari pengguna juga disarankan untuk meningkatkan akurasi dan kepuasan pengguna.
Co-Authors A.A. Ketut Agung Cahyawan W Abdul Munir Abdul Munir Abdul Munir S. Afifah Afifah Afifah Agus Halid Agus Halid, Agus Anthony Arfan Yunus Arianti Caroline Layadi Christofer Reynaldy Wijaya Ciuandi, Fudianto Dian Yustika Doni Yusri Elisya Belia Maria Engka Erwina Farel Chrisber KawiIarang Farid Wikarsa Felycia Wisanto Ferian Bagus Chandra Gabryella Angelina Ho Gunawan Halim Hamdan Arfandy Hasniati Hasniati Hasniati, Hasniati Hendra Surasa Hijaya, Daniel Alexander Hong William Fondy Huda, Andi M. Nurul Imin, Jonathan Hansen Inri Christiana Winardy izmy alwiah musdar Jason Pratama Sunarji Junaedy Kambodji, Audri Billy Kelvin Aurin Kevin Suharto Lauw Reduardy I. Laulyta, Lauw Reduardy I. Layadi, Caroline Linda Yanti Yo Lukman, Musfirah Putri m Panjiwinata, Sampurna Pratiwy aggraeny puspita sari Riany, Jane Richard Mpila Richla Rahmatika Rini Susanti and Zainuddin Rizal Syarief Ronny Jubhari Phie Joarno Sadik, Jakfar Santriawan, Bondan Sun, Enrique Justine Suryanto, Christian Rayhan Syaiful Rahman Syaiful Rahman Syamsul Bahri Takumansang, Leonardo O. J Takumansang, Leonardo O. J Wikarsa , Rex Roald Yason, Clarissa Yovita A. Maria Zulfajri B. Hasanuddin