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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JOIV : International Journal on Informatics Visualization JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING Jurnal Abdimasa TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol JMECS (Journal of Measurements, Electronics, Communications, and Systems) Jurnal Teknik Informatika (JUTIF) Jurnal Ilmu Komputer dan Informatika Journal of Informatics and Communication Technology (JICT) Jurnal Nasional Teknik Elektro dan Teknologi Informasi eProceedings of Engineering Journal of Informatics and Communication Technology (JICT)
SOFIA SAIDAH
Telkom University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Other

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Penerapan Algoritma Machine Learning YOLOv8 untuk Analisis Kepadatan dan Durasi Kehadiran Pengunjung dalam Ruangan Trisaputra , Andi Farhan; Tritoasmoro, Iwan Iwut; Saidah, Sofia
eProceedings of Engineering Vol. 12 No. 6 (2025): Desember 2025
Publisher : eProceedings of Engineering

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

Abstract

Algoritma machine learning memiliki peran penting dalam mengembangkan sistem deteksi objek, termasuk pada aplikasi people counting di ruang publik. Penelitian ini menerapkan YOLOv8 sebagai model deteksi berbasis deep learning untuk mengidentifikasi dan menghitung jumlah orang, serta mengukur durasi kehadiran mereka secara real-time. Sistem memanfaatkan aliran video dari kamera RTSP yang diproses menggunakan Python dan GPU untuk mempercepat inferensi. Model YOLOv8 digunakan untuk mendeteksi objek “person” dengan akurasi tinggi, sementara modul object tracking mempertahankan ID unik setiap individu untuk mencegah perhitungan ganda dan memungkinkan pengukuran durasi yang presisi. Data hasil deteksi dan pelacakan disimpan pada basis data lokal, lalu divisualisasikan melalui dashboard interaktif yang menampilkan jumlah pengunjung, tingkat kepadatan, dan pola kunjungan. Pengujian menunjukkan akurasi deteksi rata-rata 87,5% dengan kecepatan pemrosesan ≥15 FPS, serta toleransi kesalahan durasi ±2 detik. Implementasi ini membuktikan bahwa integrasi YOLOv8 dengan object tracking mampu menghasilkan sistem analitik berbasis data yang efektif untuk manajemen kapasitas dan perencanaan operasional. Penelitian ini juga membuka peluang pengembangan sistem people counting berbasis multi-kamera dan analisis prediktif menggunakan model machine learning yang lebih spesifik terhadap lingkungan target. Kata kunci— machine learning, YOLOv8, deteksi objek, pelacakan, analisis kepadatan.
Experimenting with the Hyperparameter of Six Models for Glaucoma Classification Muhammad Ilham; Angga Prihantoro; Iqbal Kurniawan Perdana; Rita Magdalena; Sofia Saidah
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26331

Abstract

Glaucoma, being one of the leading causes of blindness worldwide, often presents without noticeable symptoms, making early detection crucial for effective treatment. Numerous studies have been conducted to develop glaucoma detection systems. In this particular study, a glaucoma detection system using the CNN method was developed. The models employed in this study include AlexNet, Custom Layer, MobileNetV2, EfficientNetV1, InceptionV3, and VGG19. For training, an augmented RIM-ONE DL dataset was utilized. Hyperparameter experiments were conducted to determine the most optimal parameters for each model, specifically testing batch size, learning rate, and optimizer. The hyperparameter optimization process yielded the optimal parameters for each model. However, it is important to note that the MobileNetV2, InceptionV1, and VGG19 models exhibited signs of overfitting in the training graph results. Among the models, the custom layer model achieved the highest accuracy of 93%, while InceptionV3 attained the lowest accuracy at 83.5%. Testing of the models was performed using data from Cicendo Eye Hospital and the RIM-ONE DL testing dataset. Based on the testing results, it was found that InceptionV3 outperformed the other models in predicting images accurately. Therefore, the study concluded that high accuracy in training does not necessarily indicate superior performance in testing, particularly when limited variation exists in the training dataset.
Strawberry Plant Diseases Classification Using CNN Based on MobileNetV3-Large and EfficientNet-B0 Architecture Dyah Ajeng Pramudhita; Fatima Azzahra; Ikrar Khaera Arfat; Rita Magdalena; Sofia Saidah
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26341

Abstract

Strawberry is a plant that has many benefits and a high risk of being attacked by pests and diseases. Diseases in strawberry plants can cause a decrease in the quality of fruit production and can even cause crop failure. Therefore, a method is needed to assist farmers in identifying the types of diseases in strawberry plants. Currently, there are many methods to assist farmers in identifying types of disease in plants, including strawberry plants. In this study, a system is proposed to be able to detect strawberry plant diseases by classifying the disease based on healthy and diseased strawberry leaf images. The proposed system is the Convolutional Neural Network (CNN) algorithm using MobileNetV3-Large and EfficientNet-B0 models to train pre-processed datasets. The results of this study obtained the best accuracy reaching 92.14% using the MobileNetV3-Large architecture with the hyperparameter optimizer RMSProp, epochs 70, and learning rate 0.0001. The percentage of the evaluation model using MobileNetV3-Large for precision, recall, and F1-Score achieved 92.81%, 92.14%, and 92.25%.  Whereas in the EfficientNet-B0 architecture, the best accuracy results only reach 90.71% with the hyperparameter optimizer Adam, 70 epochs, and a learning rate of 0.003. Then, the precision, recall, and F1-scores for EfficientNet-B0 reached 92.65%, 90.00%, and 90.37%. Overall, it presents fairly good results in classifying strawberry leaf plant disease. Furthermore, in future work, it needs to obtain higher accuracy by generating more datasets, trying other augmentation techniques, and proposing a better model.
Co-Authors A F Akbar Abdillah, Obey Muhammad Abel Bima Wiratama Aditya S.B, I Dewa Agung Aditya, Hendra Akbar Trisnamulya Putra Al Brando Ardes Harjoko Aliefiya Rachman Alif Fajri Ryamizard Alrizqi, Naufal Dwi Andre Megantoro, Andre Megantoro Angga Prihantoro ARIS HARTAMAN Bainuri, Aulia Novria Bambang Hidayat Bambang Hidayat Bambang Hidayat Bongso, Dery Febryanto Darwindra Darwindra, Darwindra Dea Sifana Ramadhina Denny Darlis Desi Dwi Prihatin Dyah Ajeng Pramudhita Effendi , Doni Oktavian Ibnu Efri Suhartono Enrico Wiratama Purwanto Fadia Qothrunnada Fardiyanti, Defitriana Fathurrahman, Muhammad Hanif Fatima Azzahra Fellia Rizki Kusumowardani Fiera Meiristika Utami Firdaus, Muhammad Ilham Zuhruf Fitria, Ismaulida Nur Gaol, Satya Wira Fernanda Lumban Gelar Budiman Givalle , Zerricho Helsa Bagus Hidayat , Bambang Hilman Fauzi, Hilman Hurianti Vidya I Putu Yowan Nugraha Suparta Ibnu Da'wan Salim Ibnu Da’wan Salim Ubaidah Ibnu Da’wan Salim Ubaidah Ikhwanda, Alfan Ikrar Khaera Arfat Inung Wijayanto Iqbal Kurniawan Perdana Irwansyah Irwansyah Israndy Yainahu Iwan Iwut Tritoasmoro Jangkung Raharjo Kintan Veriana Koredianto Koredianto Koredianto Usman Mas, Muhammad Sabri Masykur, Muhammad Fadhel Affandi Muhamad Rokhmat Isnaini Muhammad Bayu Adinegara Muhammad Ilham Muhammad, Zalfa Alif Nabila Herman Nidaan Khofiya Nimra , Fadhil Julian Nor Kumalasari Caecar Nor Kumalasari Caecar Pratiwi Nur Alyyu Nur Ibrahim Pratama, Irsyad Fadil Augusta PRATIWI, NOR KUMALASARI CAESAR Prayudi, Yoshi Putra, Akbar Trisnamulya Putri, Tasya Busrizal Qothrunnada, Fadia R. Yunendah Nur Fu’adah Rachmat Hidayat Ashary Raditiana Patmasari Ratna Sari Ratri Dwi Atmaja Reza Ahmad Nurfauzan Richard Bina Jadi Simanjuntak Rita Magdalena Rita Magladena Rita Purnamasari Robinzon Pakpahan Salsabil Farah Aqilah Wijaya Salsabila, Afap Sangkala, Muh Aslam Mahdi Sevierda Raniprima Subiakto, Septiaini Dela Susilo, Mochammad Hilmi Suwandhi, Adhisty Putrina Syamsul Rizal Syamsul Rizal Tahta Restu Adiguna Tasya Busrizal Putri Tita Haryanti Trisaputra , Andi Farhan Tsabita Al Asshifa Hadi Kusuma Vidya, Hurianti Wahid, Gloria Shekinah Florensia Wahyu, I Komang Trisna Wibisono Sabdo Utomo WIDIANTO, MOCHAMMAD HALDI Widya Alisya Kusuma Ningrum Yunendah Fu’adah Yusnita Putri Zakiah Zakiah