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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Inspiration Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SemanTIK : Teknik Informasi Jurnal Informatika JIKO (Jurnal Informatika dan Komputer) JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknoinfo Jiko (Jurnal Informatika dan komputer) JURNAL PengaMAS Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar JURNAL IT: Media Infromasi STMIK Handayani Makassar Progresif: Jurnal Ilmiah Komputer INTI Nusa Mandiri Journal of System and Computer Engineering Information System Journal (INFOS) Jurnal Abdimas Bina Bangsa Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen JUSTIN (Jurnal Sistem dan Teknologi Informasi) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Mosfet Innovative: Journal Of Social Science Research Inspiration: Jurnal Teknologi Informasi dan Komunikasi Edumaspul: Jurnal Pendidikan
Abdul Latief Arda
Universitas Handayani Makassar
Aerospace Engineering Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Neuroscience Public Health Social Sciences Transportation Other

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IMPLEMENTASI MODEL DeiT UNTUK MEMBEDAKAN GAMBAR BUATAN AI DAN MANUSIA PADA ILUSTRASI ANIMASI 2D Erwin, Ibnu Taimiyah; Abdul Latief Arda; Imran Taufik; Muhammad Erwin Rosyadi. S; Hilyatul Auliyah Erwin
INTI Nusa Mandiri Vol. 19 No. 2 (2025): INTI Periode Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i2.6306

Abstract

The development of artificial intelligence (AI) has influenced various fields, including art and visual design. AI Generative Art, which mimics human styles, has sparked debates on originality, artistic value, as well as legal and ethical challenges. Therefore, methods are needed to distinguish between AI-generated and human-made images, particularly in 2D animation illustrations. This study proposes the use of Data-efficient Image Transformers (DeiT) for image classification. Two models tested are DeiT Base and DeiT Tiny, using a dataset of 6,000 images equally divided between AI and human categories. The dataset is split into training (70%), validation (15%), and testing (15%). Experimental results show that DeiT Base achieves over 95% accuracy with fast convergence and optimal loss function stability. Meanwhile, DeiT Tiny attains around 93% accuracy, being more computationally efficient despite requiring more epochs for stability. Compared to previous models using a larger dataset (11,000 images per category) but achieving only 80% accuracy, DeiT performs better in both accuracy and computational efficiency, even with a smaller dataset. In conclusion, DeiT is effective for classifying 2D animation images. DeiT Base excels in accuracy and convergence speed, while DeiT Tiny is more resource-efficient, making it an ideal choice for environments with computational constraints.
Analisis Metode Decision Tree dan Regresi Logistik Sebagai Sistem Rekomendasi Kenaikan Golongan Berdasarkan Kinerja Pegawai pada Universitas Lamappapoleonro Aksa, Andi Nurul; Achmad, Andani; Arda, Abdul Latief
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 15 No 1 (2025): Maret 2025
Publisher : STMIK Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33020/saintekom.v15i1.782

Abstract

This research focuses on the importance of employee performance in supporting organizational success, especially in the promotion process at Lamappapoleonro University which is still done manually. Therefore, this research aims to develop a recommendation system for promotion using the Decision Tree and Logistic Regression methods, which is expected to speed up and simplify the decision-making process regarding employee promotions. The Decision Tree algorithm is used to classify employee performance in the form of sufficient, good, and excellent variables, while the Logistic Regression algorithm is used to predict the feasibility of employee promotion with the variable feasible or inappropriate. The data used in this study includes 12 independent variables, such as attendance, discipline, responsibility, and innovative ability. The analysis results show that the Decision Tree and Logistic Regression methods are able to produce accurate predictions, with an accuracy rate of 91.67% and 100% respectively. The main factors that influence promotion are honesty, discipline, and innovation ability. With this recommendation system, the employee promotion process becomes more efficient and accurate, providing significant benefits for human resource management at Lamappapoleonro University.
Image Preprocessing Approaches Toward Better Learning Performance with CNN Tribuana, Dhimas; Hazriani; Arda, Abdul Latief
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5417

Abstract

Convolutional neural networks (CNNs) are at the forefront of computer vision, relying heavily on the quality of input data determined by the preprocessing method. An undue preprocessing approach will result in poor learning performance. This study critically examines the impact of advanced image pre-processing techniques on computational neural networks (CNNs) in facial recognition. Emphasizing the importance of data quality, we explore various pre-processing approaches, including noise reduction, histogram equalization, and image hashing. Our methodology involves feature visualization to improve facial feature discernment, training convergence analysis, and real-time model testing. The results demonstrate significant improvements in model performance with the preprocessed dataset: average accuracy, recall, precision, and F1 score enhancements of 4.17%, 3.45%, 3.45%, and 3.81%, respectively. Additionally, real-time testing shows a 21% performance increase and a 1.41% reduction in computing time. This study not only underscores the effectiveness of preprocessing in boosting CNN capabilities, but also opens avenues for future research in applying these methods to diverse image types and exploring various CNN architectures for comprehensive understanding.
Classification of Toraja Wood Carving Motif Images Using Convolutional Neural Network (CNN) Nurilmiyanti Wardhani; Asrul, Billy Eden William; Antonius Riman Tampang; Sitti Zuhriyah; Abdul Latief Arda
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 4 (2024): August 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i4.5897

Abstract

Wood carving is a cultural heritage with deep meaning and significance for the Toraja ethnic group's culture. By understanding the meaning of each Toraja carving, both tourists and the local community can gain knowledge about Toraja culture, thereby preserving and maintaining the culture amidst modern developments. Image processing approaches, particularly the development of Convolutional Neural Networks (CNN), offer a solution for extracting information from the diverse and intricate patterns of Toraja wood carvings. This study is highly significant as it implements a deep learning model using the CNN algorithm optimized with the ResNet50 architecture. The methodology in this study involves adjusting the batch size during the model training phase and applying weak-to-strong pixel transformation during the double threshold hysteresis phase in the Canny Feature Extraction process on the edges of Toraja carving images, resulting in ResNet50 architecture accurately recognizing the patterns of Toraja wood carvings. The results demonstrate significant improvements in the performance of the ResNet50 architecture with the preprocessed dataset. average precision, recall, precision, and F1-Score improvements in each Toraja carving class. For the Pa' Lulun Pao class, it was found that the precision and recall values were 0.94, and the F1-Score was 0.94. The Pa’ Somba class also showed good results, with a precision value of 0.9697, a recall of 0.96, and an F1-Score of 0.9648. The Pa’ Tangke Lumu class showed even better results, with a precision value of 0.9898, a recall of 0.97, and an F1-Score of 0.9798. The Pa’ Tumuru class also demonstrated good performance, with a precision value of 0.9327, a recall of 0.97, and an F1-Score of 0.9500. This study not only underscores the effectiveness of processing in enhancing CNN capabilities but also opens opportunities for further research in applying these methods to various image types and exploring different CNN architectures.
SISTEM MONITORING BENDUNGAN BERBASIS INTERNET OF THINGS (IoT) Hasruddin; Arda, Abdul Latief; Wardi, Wardi
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.v9i2.281

Abstract

Indonesia merupakan negara yang memiliki curah hujan cukup tinggi pada musim penghujan hampir keseluruhan daerah diguyur hujan dengan intensitas yang tinggi, sehingga perlu diwaspadai akan terjadinya banjir. Namun jika terjadi kelalaian dalam pengawasan bendungan tersebut akibatnya sangat merugikan karena menyangkut keselamatan warga disekitarnya jika terjadi meluapnya air. Oleh karena itu penulis bertujuan membuat sistem monitoring bendungan berbasi IoT, dimana sistem ini bukan hanya sebatas mengontrol ketinggian air, serta mengotomatisasi buka tutup pintu air pada bendungan, tetapi sistem monitoring ini dapat juga dikontrol setiap saat untuk disiarkan langsung atau live streaming dan diberitahukan melalui situs web ketika air meluap. Penelitian ini menggunakan metode penelitian kuantitatif dengan pendekatan eksperimen, jenis penelitian yang dapat dilakukan dalam penelitian sistem monitoring bendungan ini yaitu "eksperimen lapangan". Dari hasil pengujian, didapatkan bahwa jarak air ke sensor lebih dari 11 cm maka status yang tampil pada website adalah Normal dan pintu air dalam keadaan tertutup, jika jarak air ke sensor lebih dari atau sama dengan 10 cm dan kurang dari 11 cm maka status yang tampil pada website adalah Siaga 3, pada saat ini pula pesan whatsapp akan terkirim ke penjaga bendungan untuk menginformasikan bahwa air mengalami kenaikan, jika jarak air ke sensor lebih dari atau sama dengan 9 cm dan kurang dari 10 cm maka status yang tampil pada website adalah Siaga 2, pada saat ini pula pesan whatsapp akan terkirim ke penjaga bendungan untuk menginformasikan bahwa air mengalami kenaikan dan secara otomatis motor servo akan berputar 900 dan pintu bendungan akan terbuka setengah jika jarak air ke sensor kurang dari atau sama dengan 8 cm maka status yang tampil pada website adalah Siaga 1 dan sirine akan berbunyi serta secara otomatis motor servo akan berputar untuk membuka pintu air 1800 dan lagi pesan whatsapp akan terkirim ke penjaga pintu untuk menginformasikan bahwa ketinggian air dalam status siaga 1, serta fitur tambahan yaitu live streaming yang berfungsi sebagai pengontrol kondisi sekitar pintu bendungan apakah benar sensor ultrasonik mendeteksi kenaikan air atau tidak, kesemua alat yang digunakan dapat berfungsi dengan baik. hasil pengujian yang didapatkan nilai error rata-rata adalah 0,72% dan tingkat akurasi sebesar 99,28% dengan mengambil 15 sampel jarak dan air ditambahkan setiap 1 cm, pengujian ini dilakukan pada keadaan air yang tenang tanpa adanya gelombang atau riakan. Dan diperoleh kesimpulan bahwa pembacaan sensor dengan penggaris akurat dan alat berfungsi dengan baik.
SISTEM MONITORING BAK SAMPAH BERBASIS IoT Muh Al Hijr Asqalani; Abdul Latief Arda; Nasrullah
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.v9i2.285

Abstract

Pengawasan tempat sampah masih dilakukan secara manual, di tempat penelitian ini sering terjadi penumpukan sampah pada bak tempat sampah dimana petugas yang khusus menangani pengambilan sampah sering mengalami keterlambatan sehingga muncul bau yang tidak sedap yang dihirup oleh masyarakat dan menimbulkan penyakit yang membahayakan. Jenis penelitian ini adalah penelitian Kuantitatif dengan pendekatan eksperimental dimana ruang lingkup masalah dilakukan dengan metode studi pustaka (library research), metode pengumpulan data lapangan (field research) hasil pengujian pada alat yang dirancang, didapatkan bahwa apabila ketinggian Sampah mencapai 0-10 cm dari jarak ke tiga sensor maka Modul GSM 800L V2 akan mengirim status yang tampil pada aplikasi adalah Sampah Penuh dan apa bila hanya satu sensor membaca ketinggian sampah maka tidak akan dikirim ke aplikasi, adapun hanya bisa dikirim ke aplikasi apabila ke tiga sensor membaca sampah yang mencapai 0-10 cm Hasil Perancangan Sistem Kontrol berbasis IoT Sistem ini terdiri dari tiga sensor yaitu 2 sensor proximity dan sensor Ultrasonic dengan jarak pembacaan masing-masing 0-10 cm untuk mengontrol menggunakan android dengan notifikasi, Efektivitas Sistemnya Sistem monitoring berbasis IoT mengumpulkan data secara otomatis dari sensor-sensor yang terpasang pada bak sampah. Data ini dikirimkan secara real-time ke platform penyimpanan data, memungkinkan pemantauan yang akurat dan tepat waktu.
Evaluasi Kinerja Protokol Routing DTN untuk Skenario Smart Environment di Makassar Fakhirah, Andi Lulu; Salim, Agus; Arda, Abdul Latief
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 13, No 2 (2025)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v13i2.87066

Abstract

Penggunaan teknologi smart environment di kota Makassar menimbulkan tantangan komunikasi, terutama karena koneksi jaringan yang tidak selalu stabil. Delay Tolerant Network (DTN) adalah solusi yang memungkinkan pengiriman data meskipun jaringan sering terputus. Penelitian ini bertujuan untuk menyebarkan beberapa protokol routing DTN dalam mendukung lingkungan cerdas di Makassar, dengan mempertimbangkan jaringan yang dinamis dan mobilitas perangkat IoT serta pengguna di kota. Protokol yang diuji meliputi Epidemic Routing, PRoPHET, dan Spray and Wait. Pengujian dilakukan menggunakan ONE Simulator, dengan mengukur kinerja berdasarkan persentase pesan yang berhasil terkirim (probabilitas pengiriman), waktu tunda (latency), biaya tambahan (overhead), Jarak jaringan (hopcount). Hasilnya menunjukkan bahwa setiap protokol memiliki kelebihan dan kekurangan yang bergantung pada situasi jaringan dan pergerakan perangkat. Protokol PRoPHET lebih efisien dalam kondisi mobilitas yang teratur, sedangkan Epidemic Routing lebih baik untuk situasi darurat di mana jaringan sering terganggu. Penelitian ini memberikan panduan penting dalam memilih protokol DTN yang tepat untuk mendukung pengembangan smart environment di Makassar.
Sentiment Analysis of Instagram Comments for Monitoring Personal Branding of YBM Brilian Scholarship Recipients, Regional Office, Makassar Kherani, Riska; Arda, Abdul Latief; Jalil, Abdul; Asnimar, Asnimar; Iskandar, Akbar
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol. 15 No. 1 (2025): Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Pusat Penelitian dan Pengabdian Pada Masyarakat Sekolah Tinggi Manajemen Informatika dan Komputer AKBA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35585/inspir.v15i1.103

Abstract

This study focuses on the implementation of the Multilingual BERT (mBERT) architecture combined with a Long Short-Term Memory (LSTM) model to classify Instagram comments into positive, negative, and neutral sentiments. The primary objective is to support the monitoring of personal branding among recipients of the Bright Scholarship managed by the Baitul Mall BRILiaN Foundation (YBMRILiaN) at the Makassar Regional Office. The experimental results indicate that mBERT is capable of effectively analyzing sentiment from Instagram comments on scholarship awardees from Hasanuddin University and UIN Alauddin Makassar. Using a sample of 10 awardees, the model demonstrates a consistent increase in accuracy across epochs, achieving an average accuracy of 63.87% and a peak accuracy of 73.18% for Awardee 10, with a corresponding loss value of 1.094. These findings highlight the potential of this approach to assist scholarship organizers in systematically evaluating the personal branding of awardees on social media. Moreover, the analysis identifies one awardee whose personal branding performance may require further consideration regarding scholarship eligibility.
Analisis Sistem Monitoring dan Perancangan Alat Pendeteksi Kemiringan Tiang Listrik Dan Kerusakan Lampu Penerangan Jalan Umum (LPJU) Berbasis Internet of Things (IoT) Resky, Andi Muhammad; Wardi, Wardi; Arda, Abdul Latief
Jurnal Mosfet Vol. 5 No. 2 (2025): 2025
Publisher : Fakultas Teknik Universitas Muhammadiyah Parepare (FT-UMPAR)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31850/jmosfet.v5i2.3971

Abstract

This study aims to design and implement an Internet of Things (IoT)-based monitoring system for detecting electric pole tilt and Public Street Lighting (LPJU) failures. Stable pole structures and optimal street lighting are critical to ensure public safety and comfort, especially at night. However, manual inspections are inefficient and often fail to provide early detection of infrastructure damage. The system was developed using NodeMCU ESP8266 integrated with a potentiometer-based tilt sensor and a voltage sensor. The sensors acquire data on pole tilt and LPJU status, which are transmitted via Wi-Fi to a web-based monitoring application. Experimental procedures were carried out through laboratory testing and limited field simulations to evaluate both hardware and software performance. The test results show that the tilt sensor provides a linear response between the tilt angle and the output voltage, allowing accurate detection of pole inclination. In addition, the system successfully identified LPJU conditions (on/off) and displayed them in real-time on the monitoring dashboard. In conclusion, the proposed IoT-based monitoring system has proven reliable in detecting pole tilt and LPJU failures. This approach not only improves the efficiency of infrastructure maintenance but also contributes to the development of smart city solutions through more advanced and real-time monitoring technologies
IoT-based Soil Nutrient Monitoring and Control Using Fuzzy Logic and Multi-Modal Sensor Integration Hakis, Andi Wahyunita; Arda, Abdul Latief; Jalil, Abdul
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10575

Abstract

The decline in soil quality due to inappropriate agricultural practices has become one of the main factors contributing to reduced agricultural productivity. The primary focus of this research is on monitoring and controlling soil nutrient quality, particularly in clay soil used for chili cultivation. This study aims to develop an Internet of Things (IoT)-based monitoring system integrated with multi-modal sensors and fuzzy logic algorithms. The system is designed to support precision agriculture by enabling automated decision-making based on real-time environmental data. The research uses an experimental approach, involving the design of a system based on the ESP32 microcontroller, sensor data processing using the Mamdani fuzzy algorithm, and integration with the Blynk platform for remote monitoring and control. The system responds to changes in environmental conditions to determine optimal timing for irrigation and liquid nutrient application adaptively. The test results show that the system achieved a classification accuracy of 84% and an average F1-score of 88.5%, indicating its effectiveness in handling continuous and uncertain sensor data. Evaluation of the fuzzy logic performance revealed a 75.8% success rate in irrigation control and 99.8% accuracy in nutrient delivery, demonstrating the system’s ability to respond accurately and efficiently to actual soil and environmental conditions. With its stable, adaptive, and resource-efficient performance, this system has the potential to become a practical solution for automating irrigation and fertilization processes in support of technology-driven and sustainable agriculture.
Co-Authors Abdul jalil Abdul Rahman Agussalim, Agussalim Akbar Iskandar Akmal Hidayah A Aksa, Andi Nurul Andani Achmad Andani Achmad Andani Ahmad Andi Taufiqurrahman Akbar Andi Zulkifli Nusri Andryanto A Ansar Ansar Antonius Riman Tampang Arthanugraha, Wahyu As'ad, Miftah Fadhli ASNIMAR ASNIMAR, ASNIMAR Dhimas Tribuana Dra Najira Umar Erwin, Ibnu Taimiyah Fajar Husain Asy'ari Fakhirah, Andi Lulu Hakis, Andi Wahyunita Hasanuddin, Zulfajri B Hasruddin Hazriani Hazriani, Hazriani Hendra Hendra Hermawati Mappiwali Hilyatul Auliyah Erwin Holyness Nurdin Singadimedja Imam Akbar Imam Akbar, Imam Imran Taufik Imran Taufiq Iskandar Surdin Isman Ita Sarmita Samad Ita Sarmita Samad, Ita Sarmita J Sadly, Adnan Jusman Jusman Kahpi, Ashabul Kherani, Riska M. Risal Mashur Razak Matalangi Muh Al Hijr Asqalani Muhammad Erwin Rosyadi. S Munawirah, Munawirah Nasrullah Nasrullah Nur Iman Nurul Humaera Baharudin Purnamasari, Etika Rabi’a H Maudjik Rahmaniar Rahmaniar, Rahmaniar Resky, Andi Muhammad Rian Sultan Asizan Rijal Rijal Saidi, Muliyana Sarmila, Sarmila Sitti Sahara Syamel Sumarlina Sumarlina Supriadi Sahibu Syafruddin Syarif Syamsu Alam Taufik, Imran Tengku Riza Zarzani N Umar, Dra Najira Wardhani, Nurilmiyanti Wardi Wardi Wardi, Wardi Weya, Sermi Yuyun, Yuyun Zahir Zainuddin Zahir Zainuddin Zahir Zainuddin Zanudin Husain Zuhriyah, Sitti