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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Bulletin of Electrical Engineering and Informatics International Journal of Advances in Intelligent Informatics e-Jurnal Arus Elektro Indonesia Jurnal Inspiration JOIV : International Journal on Informatics Visualization SemanTIK : Teknik Informasi JOURNAL OF APPLIED INFORMATICS AND COMPUTING Sebatik ILKOM Jurnal Ilmiah Jiko (Jurnal Informatika dan komputer) Journal TEPAT: Teknologi Terapan untuk Pengabdian Masyarakat Simtek : Jurnal Sistem Informasi dan Teknik Komputer Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar JURNAL IT: Media Infromasi STMIK Handayani Makassar EPI International Journal of Engineering Jurnal Penelitian Enjiniring Fakultas Teknik Unhas Journal of System and Computer Engineering Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Innovative: Journal Of Social Science Research Inspiration: Jurnal Teknologi Informasi dan Komunikasi Bulletin of Social Informatics Theory and Application
Andani Achmad
Teknik Elektro UNHAS
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Design and simulation of an electric vehicle charging system with battery arrangement and control parameters optimization Pasra, Nurmiati; Samman, Faizal Arya; Achmad, Andani; Yusran, Yusran
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i4.pp2521-2537

Abstract

The development of electric vehicle (EV) charging technology requires efficient, reliable, and economical systems to address users' concerns about battery drain. This study presents a simplification of EV charger design with an isolated model and optimal battery mode setting. The research method integrates step-up Y-Δ transformers, AC-DC converters, boost DC-DC converters, integral proportional control, and battery configurations. Series (S) - parallel (P) - series (S) battery arrangement pattern to maximize system performance. The test results using a 130 mF capacitor with the S40-P2-S6 and S80-P2-S3 array patterns produced an output voltage of 946 V, while the S100-P2-S3 array pattern achieved an output voltage of 1,182 V. The system is capable of fast charging with a time of 0.2 to 2 hours for a battery capacity of 30 to 100 kWh at a charging power of 50 to 150 kW with an efficiency of up to 97%. The combination of the use of an isolated model on the charger array and the EV battery setting pattern is proven to produce stable voltage values with minimal overshoot levels, thus addressing the complex charger design challenges and battery setting needs in the 800 to 1,100 V voltage range.
Hybrid Deep Learning Approach For Stress Detection Model Through Speech Signal Chyan, Phie; Achmad, Andani; Nurtanio, Ingrid; Areni, Intan Sari
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.2026

Abstract

Stress is a psychological condition that requires proper treatment due to its potential long-term effects on health and cognitive faculties. This is particularly pertinent when considering pre- and early-school-age children, where stress can yield a range of adverse effects. Furthermore, detection in children requires a particular approach different from adults because of their physical and cognitive limitations. Traditional approaches, such as psychological assessments or the measurement of biosignal parameters prove ineffective in this context. Speech is also one of the approaches used to detect stress without causing discomfort to the subject and does not require prerequisites for a certain level of cognitive ability. Therefore, this study introduced a hybrid deep learning approach using supervised and unsupervised learning in a stress detection model. The model predicted the stress state of the subject and provided positional data point analysis in the form of a cluster map to obtain information on the degree using CNN and GSOM algorithms. The results showed an average accuracy and F1 score of 94.7% and 95%, using the children's voice dataset. To compare with the state-of-the-art, model were tested with the open-source DAIC Woz dataset and obtained average accuracy and F1 scores of 89% and 88%. The cluster map generated by GSOM further underscored the discerning capability in identifying stress and quantifying the degree experienced by the subjects, based on their speech patterns
Smart Waste Management Monitoring and Control Analysis Based on Objects Based on Smart Systems and Internet of Things Sarmila, Sarmila; Achmad, Andani; Arda, Abdul Latief
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11281

Abstract

Garbage is a problem that often becomes a trending topic in almost every country.throughout developing countries. The current condition of waste in our environment is still in a mixed condition, because the garbage has not been sorted. The minimum waste management information technology by officers also causes Waste management is slow, so that waste often piles up.The aim of this research is to develop a smart trash can that can sort metal, dry and wet waste automatically via Internet function of Things (IoT). The methodology used is Research and Development which can provide information when the trash can is full. This research was successful designing and implementing a prototype of a smart trash can based onInternet of Things (IoT) with the ability to sort waste into three categories The main components are metal, wet, and dry. The system utilizes proximity sensors inductive, soil sensor, and ultrasonic sensor HC-SR04 integrated with Blynk application for real-time monitoring of waste capacity. Algorithm Fuzzy logic is used so that the system is able to make adaptive decisions according to with the sensor condition. from the performance in the research Where the Accuracy of the system is 97.10%. The calculation is based on the number of correct predictions on the diagonal. main data divided by total data: true = 189 (Dry) + 187 (Wet) + 194 (Metal) = 570 out of a total of 587 samples, so 570/587 = 0.9710 (97.10%), with 17 error (error rate 2.90%). These values describe how much the accuracy and completeness of the model in recognizing each category of waste, with results consistently high (average 0.97).
Deepfake Detection in Videos Using Long Short-Term Memory and CNN ResNext Abidin, Muhammad Indra; Nurtanio, Ingrid; Achmad, Andani
ILKOM Jurnal Ilmiah Vol 14, No 3 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i3.1254.178-185

Abstract

Deep-fake in videos is a video synthesis technique by changing the people’s face in the video with others’ face. Deep-fake technology in videos has been used to manipulate information, therefore it is necessary to detect deep-fakes in videos. This paper aimed to detect deep-fakes in videos using the ResNext Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) algorithms. The video data was divided into 4 types, namely video with 10 frames, 20 frames, 40 frames and 60 frames. Furthermore, face detection was used to crop the image to 100 x 100 pixels and then the pictures were processed using ResNext CNN and LSTM. The confusion matrix was employed to measure the performance of the ResNext CNN-LSTM algorithm. The indicators used were accuracy, precision, and recall. The results of data classification showed that the highest accuracy value was 90% for data with 40 and 60 frames. While data with 10 frames had the lowest accuracy with 52% only. ResNext CNN-LSTM was able to detect deep-fakes in videos well even though the size of the image was small.
Augmented Reality 3D untuk Pengenalan Organ Tubuh Manusia Achmad, Andani; Zainuddin, Zahir; Husain, Muhammad Fadhil
ILKOM Jurnal Ilmiah Vol 12, No 3 (2020)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v12i3.680.233-240

Abstract

Marker Augmented Reality 3D pada Aplikasi Pengenalan Organ Tubuh Manusia adalah untuk membuat model belajar menggunakan teknologi Augmented Reality guna menyampaikan informasi tentang Pengenalan Organ Tubuh Manusia yang lebih interaktif dan inovatif dengan Teknologi Augmented Reality ke dalam satu aplikasi Android. Dalam mendukung penelitian ini digunakan Aplikasi Blender untuk pembuatan 3D Modelling dan aplikasi unity untuk pembuatan sistem yang di dukung dengan Bahasa Pemrograman C#, Vuforia SDK dalam implementasi Augmented Reality.  Hasil pengujian deteksi 3D marker oleh kamera dapat di simpulkan bahwa tiap objek 3D memiliki jarak deteksi yang berbeda-beda. Hasil pengujian Virtual reality menunjukkan bahwa semua fungsi yang di ujikan dapat berjalan sesuai keinginan.
Co-Authors -, Sofyan Abd. Salam Abdul Latief Arda Abdul Muis Abdullah, Alfiah Abidin, Muhammad Indra Achmad Zubair Adnan Adnan Ahmad Abdullah Ahmad Ilham, Amil Akbar Iskandar Akhmad Qashlim, Akhmad Aksa, Andi Nurul Al Kautsar Amil Ahmad Ilham Amriana Amriana Andini Dani Achmad Andini Dani Achmad, Andini Dani Ansar Ansar Ansar Suyuti Anshar, Muh Arda, Abdul Latif Ardiaty Arief . Areni, Intan Sari Arief, Ardiaty Arief, Azran Budi Armin Lawi Asnimar Awal Kurniawan Azran Budi Arief Baital, Muhammad Syarif Bakrim, La Ode Basri Basri Basri, - Budiansyah, Anugrah Christoforus Y. Deny Wiria Nugraha Dewi Kusumawati, Dewi Dewiani . Dewiani Dewi Djamaluddin Dewiani Dewiani Dhimas Tribuana Edwin Adrin Wihelmus Sanad Ejah Umraeni Elyas Palantei Faizal A. S. Faizal A. Samman . Faizal Arya Samman Faizal Arya Samman Fighi S. Permadi . Figur Muhammad Gassing - Gassing . Hasanuddin, Zulfajri Basri Hazriani, Hazriani Husain, Muhammad Fadhil Ida Rachmaniar Sahali Ida Rachmaniar Sahali Indrabayu Indrabayu Indrabayu, - Ingrid Nurtanio Intan Sari Areni Irma Pratiwi Sayuti Konate, Siaka Latif, Nuraida M. Hasanuddin Mansyur Martani, Ahmad Merna Baharuddin Merna Baharuddin Milleneo . Mubarak, Abdul Muh Anshar Muh. Anshar . Muhammad Abdillah Rahmat Muhammad Akbar Muhammad Niswar Nappu, Muhammad Bachtiar Palantei, Elyas Palantei, Idris Panggalo, Samuel Pasra, Nurmiati Phie Chyan Rachmaniar, Ida Rahman, Ariastuti Ramdan Satra Rhiza S. Sadjad Rhiza S. Sadjad . Rifaldy Ramadhan Latief S, Mulyadi Salam, Andi Ejah Umraeni Salama Manjang Samuel Panggalo Sarmila, Sarmila Suliman, Suliman Supriadi Sahibu Syafruddin Syarif Syafruddin Syarif Tajuddin Waris Usman Usman Utomo, Tri Panji Sugi Wahyudi Sofyan Wardi . Wardi Wardi Yudha, Muh. Reza Eka Yulis, Nurlina Yusran . Yusran Yusran Yuyun Yuyun, Yuyun Zaenab . Zahir Zainuddin Zahir Zainuddin