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All Journal Infotech Journal JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Informatika Universitas Pamulang PROCEEDING STIMA MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer BERNAS: Jurnal Pengabdian Kepada Masyarakat Tematik : Jurnal Teknologi Informasi Komunikasi Decode: Jurnal Pendidikan Teknologi Informasi Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Jurnal INFOTEL Jurnal Informatika: Jurnal Pengembangan IT SEMINAR TEKNOLOGI MAJALENGKA (STIMA) International Journal of Applied Information Systems and Informatics
Ardi Mardiana
Department Of Informatics, Faculty Of Engineering,Universitas Majalengka, Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

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RANCANG BANGUN MODEL PENGENALAN EMOSI SUARA UNTUK PROMOSI PARIWISATA DI MAJALENGKA Rifki, Muhammad; Bastian , Ade; Mardiana, Ardi
SEMINAR TEKNOLOGI MAJALENGKA (STIMA) Vol 9 (2025): Seminar Teknologi Majalengka (STIMA) 9.0 Tahun 2025
Publisher : Universitas Majalengka

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Abstract

Emotion plays a crucial role in human communication, and recognizing emotions from speech (Speech Emotion Recognition or SER) has broad applications in various fields. This study designs and implements a speech emotion recognition application to support tourism promotion events in Majalengka. A 1D Convolutional Neural Network (CNN) model is developed using public SER datasets (RAVDESS, CREMA-D, SAVEE, TESS) combined and augmented to improve cultural generalization. Key audio features, such as Mel Frequency Cepstral Coefficients (MFCC), are extracted for effective emotion classification. The resulting system achieves an accuracy of 74.4% on test data, successfully recognizing emotions like angry, sad, neutral, and happy with good precision. This automated emotion analysis assists judges in evaluating participants’ speeches objectively and efficiently. The integration of SER technology in tourism events demonstrates an innovative strategy to enhance the promotion of local culture and improve the overall visitor experience in Majalengka.
PENGISIAN BAHAN BAKAR SELF-SERVICE DENGAN PEMBAYARAN NON TUNAI MELALUI ANIMASI 3D BLENDER Adriansyah, Rizky Adin; Mardiana, Ardi
SEMINAR TEKNOLOGI MAJALENGKA (STIMA) Vol 9 (2025): Seminar Teknologi Majalengka (STIMA) 9.0 Tahun 2025
Publisher : Universitas Majalengka

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Abstract

This study focuses on the development of a 3D animation as an educational medium for the selfservice fuel filling system integrated with cashless payment methods at gas stations. The research was motivated by the limited adoption of self-service systems in Indonesia and the increasing need for safe, convenient, and contactless transactions, especially after the COVID-19 pandemic. The study employed the Multimedia Development Life Cycle (MDLC) method consisting of six stages: concept, design, material collecting, assembly, testing, and distribution. The testing phase was carried out in two stages: Black box Testing to evaluate functionality and usability testing with 40 respondents using a questionnaire. The results showed that the developed animation achieved user satisfaction levels of 76.75% for ease of understanding, 75.00% for cashless payment integration, and 82.50% for effectiveness as an educational tool, with an overall average of 77.84%. These findings indicate that the 3D animation is feasible and effective as a learning medium to improve user understanding of self-service fuel filling with cashless payment at gas stations.
PERANCANGAN SISTEM INFORMASI MANAJEMEN DISTRIBUSI AGEN GAS ELPIJI BERBASIS WEB PADA PT. BAROKAH TIGA PUTRA Kiranti, Mei Bunga; Mardiana, Ardi
SEMINAR TEKNOLOGI MAJALENGKA (STIMA) Vol 9 (2025): Seminar Teknologi Majalengka (STIMA) 9.0 Tahun 2025
Publisher : Universitas Majalengka

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Abstract

Barokah Tiga Putra is an LPG (Liquefied Petroleum Gas) agent in Majalengka Regency that distributes 3-kilogram LPG cylinders to more than 60 distribution bases. The current manual distribution process using Microsoft Excel leads to several problems, such as slow data access, recording errors, and the inability to monitor distribution in real time. This research aims to design and implement a web-based distribution management information system to improve efficiency, accuracy, and operational effectiveness. The system development method applied is Rapid Application Development (RAD), which consists of requirement planning, design, testing, and implementation stages. The implementation results show that the system successfully facilitates the management of base data, requests, deliveries, sales, and LPG receipts in an integrated manner. Furthermore, the system enables real-time monitoring of distribution, supporting agents in making strategic decisions. Therefore, the developed information system provides a practical solution to overcome the limitations of the previous manual distribution process.
RANCANG BANGUN WEBSITE KETERSEDIAAN OBAT UNTUK MENINGKATKAN AKSES DAN LAYANAN KESEHATAN Amelia, Rizki; Mardiana, Ardi
SEMINAR TEKNOLOGI MAJALENGKA (STIMA) Vol 9 (2025): Seminar Teknologi Majalengka (STIMA) 9.0 Tahun 2025
Publisher : Universitas Majalengka

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Abstract

The healthcare industry requires an efficient drug inventory management system to improve service quality and patient access to medicines. This research aims to develop a web-based drug availability application using the Rapid Application Development (RAD) methodology. The system includes drug inventory management, patient data management, transaction recording (incoming and outgoing drugs), and automatic reporting features. This web-based application was developed using NextJS framework, JavaScript programming language, and MySQL database to provide a user-friendly interface for clinic staff. The development methodology follows the RAD approach which consists of requirements planning, user design, construction, and cutover phases. Black box testing results show that all system functions work properly with 100% success rate. The system successfully provides automated drug inventory management, transaction monitoring, and comprehensive reporting features that can improve operational efficiency at Guvili Clinic. This drug availability management system offers an effective solution to replace manual recording methods and provides real-time access to drug inventory data for better healthcare service management.
Assessing The Impact of Water Quality on Freshwater Aquaculture: A Systematic Literature Review Bastian, Ade; Mardiana, Ardi; Koswara, Engkos; Rifki, Muhammad
Journal of Applied Information System and Informatic (JAISI) Vol 3, No 1 (2025): MEI 2025
Publisher : Deparment Information System, Siliwangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/jaisi.v3i1.14180

Abstract

Water quality is a critical determinant of ecosystem sustainability and the productivity of freshwater aquaculture. This study performed a systematic literature review (SLR) to assess the impact of water quality on the growth, health, and yield of freshwater fish. The research examines contemporary technical advancements, such as Internet of Things (IoT) systems, unmanned aerial vehicles (UAVs), machine learning prediction models, and biotechnological methods for monitoring and managing water quality. The literature selection procedure employed the PRISMA framework and encompassed 136 articles sourced from the SCOPUS database. Following rigorous screening processes, three primary publications were chosen for additional examination. The review findings indicate that parameters like dissolved oxygen (DO), pH, ammonia, and temperature significantly influence fish health and production. Contemporary technologies, such IoT and UAVs, have demonstrated their capacity to enhance the effectiveness of water quality monitoring, whilst biotechnology provides novel options for the sustainable treatment of aquaculture waste. This research offers significant insights for scholars, policymakers, and practitioners in the advancement of more efficient and sustainable aquaculture methodologies.
Optimized YOLOv8 Model for Accurate Detection and Quantificationof Mango Flowers Ardi Mardiana; Ade Bastian; Ano Tarsono; Dony Susandi; Safari Yonasi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i3.4614

Abstract

Mangoes are widely cultivated and hold significant economic value worldwide. However, challenges in mango cultivation, such as inconsistent flowering patterns and manual yield estimation, hinder optimal agricultural productivity. This study addresses these issues by leveraging the You Only Look Once (YOLO) version 8 object detection technique to automatically recognize and quantify mango flowers using image processing. This research aims to develop an automated method for detecting and estimating mango yields based on flower density, representing the early stage of the plant growth cycle. The methodology involves utilizing YOLOv8 object detection and image processing techniques. A dataset of mango tree images was collected and used to train a CNN-based YOLOv8 model, incorporating image augmentation and transfer learning to improve detection accuracy under varying lighting and environmental conditions. The results demonstrate the model’s effectiveness, achieving an average mAP score of 0.853, significantly improving accuracy and efficiency compared to traditional detection methods. The findings suggest that automating mango flower detection can enhance precision agriculture practices by reducing reliance on manual labor, improving yield prediction accuracy, and streamlining monitoring techniques. In conclusion, this study contributes to the advancement of precision agriculture through innovative approaches to flower detection and yield estimation at early growth stages. Future research directions include integrating multispectral imaging and drone-based monitoring systems to optimize model performance further and expand its applications in digital agriculture.
IMPLEMENTASI ALGORITMA EXPONENTIAL SMOOTHING UNTUK PREDIKSI PENJUALAN PADA PLATFORM E-COMMERCE Ardi Mardiana; Muhammad Iqbal Assegaf; Nunu Nurdiana
INFOTECH journal Vol. 9 No. 1 (2023)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/infotech.v9i1.5054

Abstract

Peramalan memiliki peran penting dalam mencapai tujuan secara efektif dan efisien bagi sebuah perusahaan. Penelitian ini bertujuan untuk menganalisis pola penjualan produk di platform e-commerce dengan menggunakan metode exponential smoothing. Penelitian ini juga menggunakan MAD, MSE, dan MAPE untuk menghitung tingkat kesalahan. Hasil penelitian menunjukkan bahwa metode exponential smoothing dengan alpha 0.9 memberikan error paling kecil dibandingkan dengan alpha lainnya. Penelitian ini menemukan bahwa peramalan penjualan produk di bulan November tidak akan berbeda jauh dengan penjualan pada bulan Oktober. Jika perusahaan menerapkan metode peramalan ini, penjualan akan optimal dan kelebihan atau kekurangan stok dapat dihindari sehingga target penjualan dapat tercapai. Selain itu, biaya produksi hingga penjualan akan lebih efisien. Hasil peramalan menunjukkan nilai MAD sebesar 24.90, MSE sebesar 153.12, dan MAPE sebesar 5.61% dengan peramalan sebesar 4.87 pcs
PERSPEKTIF GLOBAL TREN DAN PERKEMBANGAN INOVASI PENELITIAN VIDEO TO MUSIC GENERATION Ade Bastian; Ardi Mardiana; Muhammad Fahmi Ajiz; Satria Winata
INFOTECH journal Vol. 11 No. 1 (2025)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/infotech.v11i1.13830

Abstract

Penelitian ini bertujuan memetakan evolusi generasi musik berbasis AI, khususnya generasi musik dari video. Melalui analisis bibliometrik terhadap 999 publikasi ilmiah (1997-2025), kami menganalisis tren dan struktur konseptual menggunakan VOSviewer. Metode meliputi ekstraksi metadata, konstruksi jaringan ko-kepengarangan, dan identifikasi kluster dominan. Hasil mengungkapkan lima kluster tematik utama: model generatif berbasis teks, generasi musik simbolik, musik video game, integrasi multimedia, dan komposisi otomatis. Studi terbaru menunjukkan pergeseran ke arsitektur generatif multimodal, mengintegrasikan transformer dan model difusi untuk mengatasi tantangan penyelarasan semantik-temporal antara video dan musik. Penelitian mengidentifikasi kesenjangan utama: kelangkaan dataset berpasangan skala besar, kurangnya metrik evaluasi standar, dan terbatasnya sistem generasi real-time. Kebaruan penelitian ini adalah pemetaan bibliometrik pertama yang fokus eksklusif pada generasi musik dari video, memberikan fondasi bagi komunitas akademik dan industri untuk memahami lintasan dan arah masa depan bidang ini.
EXPLAINABLE DEEP LEARNING FOR BEEF FRESHNESS CLASSIFICATION USING GRAD-CAM VISUALIZATION Ade Bastian; Ardi Mardiana; Billy Adrian Fernanda; Harun Sujadi; Abrar Wahid; Riri Nurazizah; Wildan Zhilal Manafi
INFOTECH journal Vol. 11 No. 2 (2025)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/infotech.v11i2.16897

Abstract

Kesegaran daging sapi merupakan faktor kritis bagi keamanan pangan di Indonesia, mengingat tingginya tingkat konsumsi dan impor komoditas ini. Metode penilaian kesegaran tradisional seringkali lambat, destruktif (merusak), atau bias secara subjektif. Penelitian ini bertujuan untuk mengembangkan model Deep Learning yang tidak hanya akurat dalam mengklasifikasikan kesegaran daging sapi (Segar, Setengah Segar, Busuk) tetapi juga dapat dijelaskan (explainable) dalam proses pengambilan keputusannya. Kami menerapkan Transfer Learning menggunakan arsitektur Convolutional Neural Network (CNN) yang ringan, yaitu MobileNetV2, pada dataset yang terdiri dari 2.266 citra daging yang telah diaugmentasi. Untuk mengatasi sifat "black-box" dari CNN, Gradient-weighted Class Activation Mapping (Grad-CAM) diimplementasikan untuk memvisualisasikan area fokus model. Hasil eksperimen menunjukkan bahwa model kami yang telah di-fine-tune mencapai akurasi validasi yang tinggi (96,01%), dengan presisi sempurna (100%) untuk kelas 'Busuk' (Spoiled), memastikan tidak ada daging busuk yang salah diklasifikasikan sebagai daging segar. Analisis Grad-CAM lebih lanjut memvalidasi bahwa model mendasarkan keputusannya pada fitur visual yang relevan secara biologis, seperti pola perubahan warna dan tekstur permukaan, bukan pada noise latar belakang. Temuan ini mengonfirmasi potensi integrasi CNN ringan dengan XAI untuk sistem kontrol kualitas yang andal, non-destruktif, dan transparan dalam industri pangan.
Mapping The Landscape of Speech Processing Research: : Trends, Insights, and Emerging Directions Ardi Mardiana; Ade Bastian; Muhamamad Rifki; Eka Tresna Irawan
Jurnal Informatika Universitas Pamulang Vol 10 No 1 (2025): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jiup.v10i1.48093

Abstract

Speech processing has become a significant study domain within signal processing, artificial intelligence, and human-computer interaction. This work does a bibliometric analysis to ascertain research trends, notable problems, and prospective directions in voice processing. We assess significant research outputs, including publication growth, influential authors, renowned journals, and collaboration networks during the last two decades, using data sourced from credible scientific sources such as Scopus and Web of Science. The results underscore notable progress in automated voice recognition, speaker identification, and speech synthesis, while simultaneously confronting ongoing issues associated with multilingual datasets, noise resilience, and resource efficiency. Moreover, new technologies, such deep learning and neural architecture search, are recognized as catalysts for future developments. This bibliometric study seeks to provide scholars and practitioners with a thorough overview of the existing environment and strategic insights for the advancement of the voice processing domain.
Co-Authors Aas Nuraisah Abdurahman, Dede Abrar Wahid Aceng Jarkasih Ade Bastian Adriansyah, Rizky Adin Aji Permana Ano Tarsono Asep Rachmat Ayu Sunengsih Azizah Nurzakiyah Berliani, Mega Billy Adrian Fernanda Budiman Citra Pebrianti Fitria Dadan Zaliluddin Dede Didin Dede Maulana Deffy Susanti Deffy Susanti Dinda Sri Wulansari Dony Susandi Eka Tresna Irawan Eka Tresna Irawan Elawati Enang Rusnandi Enceng Enda Suhada Endah Prihartini Fahmi Aziz, Muhamamad Fauzan Faturahman Azkia Firmansyah, Mochammad Bagasnanda Fitriyani, Rofi Gary Gibran Amnesty Geraldy Fatullah Hafsari, Zacky Harun Sujadi Ii Sopiandi, Ii Iis Caswini Intan Kusumadewi Irawan, Eka Tresna Jamhari Jamhari Kiranti, Mei Bunga Koswara, Engkos Mega Berliani Mochammad Bagasnanda Firmansyah Muhamamad Rifki Muhammad Dziya Dhiyauddin Muhammad Fahmi Ajiz Muhammad Iqbal Assegaf Muhammad Rifki Muhammad Rifki Muslimah, Dinda Desmonda Nisa Farhatunnisa Nunu Nurdiana Nunu Nurdiana Pangestu, Arki Aji Permana, Sri Mentari Widya Ningrum Priyadi, Deni Puput Putriani Ridwan, Nugraha Sugiana Rifki, Muhamamad Riri Nurazizah Risa Risnawati Risma Miftahul Ulum, Ade Rizki Amelia Safari Yonasi Sandi Fajar Rodiyansyah Sandi Fajar Rodiyansyah Satria Winata Sri Mentari Widya Ningrum Permana Syifaa Puspita Rahayu Tarsono, Ano Tri Ferga Prasetyo Wahid, Abrar Wahono, Syiffa Safiera Wildan Zhilal Manafi Wulan Dari, Tiara