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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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SISTEM MONITORING PERGERAKAN KAPAL NELAYAN TRADISIONAL MENGGUNAKAN INTERNET OF THINGS Rahman, Ariastuti; Achmad, Andani; Arda, Abdul Latif; Qashlim, Akhmad
JURNAL ILMU KOMPUTER Vol 9 No 2 (2023): Edisi September
Publisher : LPPM Universitas Al Asyariah Mandar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35329/jiik.v9i2.286

Abstract

Penelitian ini bertujuan untuk (1) Melakukan perancangan Sistem Monitoring Pergerakan Kapal Nelayan Tradisional Menggunakan IoT. (2) Menganalisis hasil monitoring pergerakan kapal nelayan tradisional mengguankan IoT. Penelitian ini dilaksanakan pada wilayah perairan teluk mandar kabupaten Polewali Mandar, Metode Experimental Fishing dengan memasang alat IoT pada kapal nelayan digunakan untuk melihat langsung jalur penangkapan ikan. Pengumpulan data dilakukan pada dua tempat yakni pada Kampung Nelayan Desa Tonyaman dan Kampung Nelayan Kelurahan Takkatidung hal ini dimaksudkan untuk melihat fungsionalitas perangkat IoT, data koordinat, suhu, dan kecepatan angin yang berbeda. Pengujian dan kalibrasi dilakukan dengan membandingkan perangkat IoT dengan alat standar. Hasil penelitian menunjukkan bahwa koordinat pergerakan 2 kapal nelayan yang dimonitoring telah berhasil terdeteksi menggunakan perangkat IoT pada setiap 2 detik, begitupun informasi suhu dan kecepatan angin kemudian disajikan pada web sistem monitoring. Jalur yang dilalui oleh nelayan tradisional umumnya pada wilayah yang dekat dari sisi pulau untuk menangkap spesies tertentu, dengan kondisi suhu dengan nilai rata 29-33⁰ siang hari dan kecepatan angin rata-rata 24 m/s. Nelayan dengan ukuran kapal 5 GT tetap berada pada jalur 1 dengan jarak 2 Mil atau 3 Km dari garis pantai sehingga perangkat masih terhubung oleh sinyal wifi antena BTS dengan baik. Hasil penelitian ini dapat menjadi rekomendasi bagi instansi terkait sebagai alternatif alat pantau berbiaya rendah dari VMS (vessel monitoring sistem) pada umumnya, perangkat IoT dapat dipadukan dengan sensor windvane untuk membaca arah angin dan sensor ultrasonik untuk deteksi keberadaan ikan
Enhancing Relational Database Efficiency through Algorithmic Query Tuning in Virtual Memory Systems Yulis, Nurlina; Ilham, Amil Ahmad; Achmad, Andani; Samman, Faizal Arya
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.2850

Abstract

The rapid evolution of virtual memory-based relational database systems has significantly advanced data processing capabilities. However, the efficiency of these systems largely depends on query execution optimization, which can be enhanced through algorithmic query tuning techniques. This study investigates the impact of these techniques on enhancing query performance in virtual memory-based relational databases. Various algorithmic methods were analyzed to optimize query execution plans, with a focus on key performance indicators such as execution time, CPU and memory usage, disk I/O, and cache hit ratio. The systematic application of these methods revealed effective strategies for performance enhancement. Results show substantial improvements in execution time, resource utilization, and scalability. This work offers valuable insights for database administrators and system architects, highlighting the role of algorithmic query tuning in managing the growing demands for data processing. Future research endeavors should explore the realm of AI-driven automation, with a particular focus on enhancing query optimization techniques. Additionally, there is a pressing need to investigate advanced security measures that safeguard data integrity within expansive, large-scale systems. By adopting innovative approaches, we can ensure robust protection and efficient performance in an increasingly data-driven world.
Rancang Bangun Sistem Navigasi Robot Otonom Waiter-Bot Berbasis Robot Operating System Salam, Abd.; Anshar, Muh; Achmad, Andani; Yudha, Muh. Reza Eka
Jurnal Arus Elektro Indonesia Vol. 10 No. 3 (2024)
Publisher : Fakultas Teknik, Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/jaei.v10i3.47685

Abstract

Perkembangan teknologi robotika semakin masif diterapkan di industri manufaktur seiring memasuki era revolusi industri 5.0 dalam membantu meringankan beban manusia serta meningkatkan efisiensi waktu dan tenaga. Salah satu bentuk penerapannya pada penelitian robot otonom Waiter-Bot sebagai robot cerdas bebasis Robot Operating System (ROS) dengan misi pengantaran logistik secara autonomus. Penelitian ini menggunakan metode kuantitatif dengan pengujian robot melakukan perencanaan jalur navigasi dalam mencapai misi. Metode perencanaan jalur navigasi terdiri atas perencanaan jalur lokal (local palnner) dan jalur global (global planner) untuk memperoleh jalur trayektori lintasan paling terdekat dan teraman. Pengujian global planner dilakukan dengan menggunakan dua skenario. Skenario 1, robot melakukan navigasi dengan kemampuan mengenali batas jalur lintasan. Adapun skenario 2, robot diberikan perlakukan penambahan objek diluar hasil pemetaan untuk mengenali dan menghindari obstacle tersebut. Pengujian local planner menggunakan algoritma Dynamic Window Approach (DWA) untuk mengetahui kemampuan robot merencanakan opsi jalur baru dalam kondisi situasional agar tetap bergerak otonom untuk mencapai target misi. Hasil yang diperoleh dari penelitian ini yakni pada pengujian global planner, waktu tempuh rata-rata saat robot melewati misi A,B,C dengan jarak tempuh 12,02 meter pada skenario 1 yakni 3,81 menit serta skenarion 2 dengan waktu tempuh rata-rata 4 menit 12 detik. Sedangkan pengujian local planner,diperoleh waktu transisi pivot dengan rata-rata 73 detik. Dalam sistem navigasi robot berbasis ROS pada penelitian ini diketahui jumlah node yang aktif yakni 14 node dalam 8 topik. Dengan demikian navigasi Waiter-Bot dapat menjalankan misi dengan efektif.
An Eccentricity for Improvement in Rice Stem Borer Detection Using Sensed Drone Imaging Indrabayu, -; Basri, -; Achmad, Andani
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.2864

Abstract

Rice stem borers are severe pests that cause significant crop losses. This research aimed to tackle this problem by using a drone equipped with a high-resolution camera to capture detailed images of paddy fields. These images were then processed to estimate the early potential attacks of stem borer pests through color segmentation computing. The detection process relied on analyzing color variations, particularly focusing on symptoms indicative of stem borer presence. The system utilized Hue, Saturation, Value (HSV) color segmentation and advanced image processing algorithms on numerous rice field videos collected from drone flights conducted at altitudes ranging from 5 to 40 meters above the ground. To improve detection accuracy, the study tested the system with and without the eccentricity parameter, which is crucial in eliminating false positives caused by the misidentification of field embankments as stem borers. This research's primary contribution is the implementation of eccentricity, which significantly reduces the false-positive rate. The results demonstrated that the accuracy of the system with the eccentricity parameter included was 75%, compared to a significantly lower accuracy rate of 17.19% when the eccentricity parameter was not used. Overall, this study highlights the effectiveness of using drones for remote sensing and the importance of incorporating eccentricity in image processing algorithms to enhance the precision of early stem borer detection in rice fields. This approach not only improves the reliability of pest detection but also offers a promising method for protecting rice crops from severe pest damage.
Sistem Kendali Ayunan Bayi Berbasis Mikrokontroller dan Android Abdullah, Ahmad; Zainuddin, Zahir; Achmad, Andani
Journal of System and Computer Engineering Vol 4 No 2 (2023): JSCE: Juli 2023
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v4i2.889

Abstract

This study aims to design a prototype of a microcontroller and arduino-based baby swing control system that will be very useful for housewives who have babies. This system will automatically work to move the swing when the sound of a baby and water is detected. This system is designed using a sound sensor that functions to detect the baby's voice, a water sensor to detect water when a baby is urinating, a DC motor that functions to move the swing automatically and a mobile application that functions to monitor swings. The test results show that the tool functions as desired, where when the tool detects the sound of a baby's cry, the tool automatically moves the swing, when the tool detects the presence of water, the swing will send a notification to the smartphone, this tool also successfully displays information to the smartphone in the form of a state baby in the form of video and sensor conditions on the swing. The communication distance between the smartphone and the device is 20 meters.
Prototype Robot Pengantar Obat Berbasis IOT RSUD Mamuju Ansar, Ansar; Achmad, Andani; Arda, Abdul Latief
Innovative: Journal Of Social Science Research Vol. 4 No. 6 (2024): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v4i6.16281

Abstract

Penelitian ini bertujuan untuk merancang dan menguji prototipe robot pengantar obat yang berfungsi untuk mengantarkan obat ke setiap kamar pasien dengan mengikuti jalur yang telah ditentukan. Robot ini dilengkapi dengan teknologi RFID yang digunakan sebagai pembaca tanda untuk jalur kiri dan kanan, serta sensor infrared untuk mendukung navigasi yang akurat. Berdasarkan hasil pengujian, robot ini mampu beroperasi sesuai rencana, menunjukkan efisiensi dalam waktu tempuh dan kapasitas beban di berbagai kondisi dan kecepatan. Temuan ini menunjukkan bahwa robot pengantar obat siap diimplementasikan dalam lingkungan praktis sebagai solusi inovatif yang aman, efisien, dan andal dalam pengantaran obat kepada pasien.
Performance Analysis of Feature Mel Frequency Cepstral Coefficient and Short Time Fourier Transform Input for Lie Detection using Convolutional Neural Network Kusumawati, Dewi; Ilham, Amil Ahmad; Achmad, Andani; Nurtanio, Ingrid
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2062

Abstract

This study aims to determine which model is more effective in detecting lies between models with Mel Frequency Cepstral Coefficient (MFCC) and Short Time Fourier Transform (STFT) processes using Convolutional Neural Network (CNN). MFCC and STFT processes are based on digital voice data from video recordings that have been given lie or truth information regarding certain situations. Data is then pre-processed and trained on CNN. The results of model performance evaluation with hyper-tuning parameters and random search implementation show that using MFCC as Voice data processing provides better performance with higher accuracy than using the STFT process. The best parameters from MFCC are obtained with filter convolutional=64, kerneconvolutional1=5, filterconvolutional2=112, kernel convolutional2=3, filter convolutional3=32, kernelconvolutional3 =5, dense1=96, optimizer=RMSProp, learning rate=0.001 which achieves an accuracy of  97.13%, with an AUC value of 0.97. Using the STFT, the best parameters are obtained with filter convolutional1=96, kernel convolutional1=5, convolutional2 filters=48, convolutional2 kernels=5, convolutional3 filters=96, convolutional3 kernels=5, dense1=128, Optimizer=Adaddelta, learning rate=0.001, which achieves an accuracy of 95.39% with an AUC value of 0.95. Prosodics are used to compare the performance of MFCC and STFT. The result is that prosodic has a low accuracy of 68%. The analysis shows that using MFCC as the process of sound extraction with the CNN model produces the best performance for cases of lie detection using audio. It can be optimized for further research by combining CNN architectural models such as ResNet, AlexNet, and other architectures to obtain new models and improve lie detection accuracy.
Transformers for aerial images semantic segmentation of natural disaster-impacted areas in natural disaster assessment Wiria Nugraha, Deny; Ahmad Ilham, Amil; Achmad, Andani; Arief, Ardiaty
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8454

Abstract

Aerial image segmentation of natural disaster-impacted areas and detailed and automatic natural disaster assessment are the main focus of this study. Detecting and recognizing objects on aerial images of areas impacted by natural disasters and assessing natural disaster-impacted areas are still difficult problems. To solve these problems, this study utilizes four of the latest transformer-based semantic segmentation network models, bidirectional encoder representation from image transformers (BEIT), dense prediction transformer (DPT), OneFormer, and SegFormer, and proposes a detailed and automatic natural disaster assessment of the segmented image. The SegFormer model achieved the first-best result, and the OneFormer model achieved the second-best result. The SegFormer model outperformed OneFormer by 1.58% higher for the mean accuracy value and 4.28% for the mean intersection over union (mIoU) value. All receiver operating characteristics (ROC) curves have mean area under curve (AUC) values above 0.9, which means that the SegFormer model performs well in generating semantic segmentation images. The fuzzy c-means (FCM) clustering algorithm performed well and could automatically cluster the natural disaster assessments into four categories. This study has produced semantic segmentation of aerial images of areas impacted by natural disasters and natural disaster assessments, which can be used in natural disaster management systems.
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
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 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 . 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 Milleneo . Mubarak, Abdul Muh Anshar Muh. Anshar . Muhammad Abdillah Rahmat Muhammad Akbar Muhammad Niswar Nappu, Muhammad Bachtiar Pasra, Nurmiati Phie Chyan 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 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