Article Per Year (5 Year)
p-Index From 2021 - 2026
3.874
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Applied Sciences Jurnal Ilmiah Informatika Komputer Jurnal Ilmiah Teknologi dan Rekayasa UG Journal Telematika Scientific Journal of Informatics CESS (Journal of Computer Engineering, System and Science) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Jurnal Ilmiah KOMPUTASI JURNAL MEDIA INFORMATIKA BUDIDARMA CogITo Smart Journal Jurnal Informatika Universitas Pamulang ILKOM Jurnal Ilmiah Jurnal Nasional Teknik Elektro dan Teknologi Informasi ICONESTH Jurnal Pengabdian Kepada Masyarakat Darma Saskara Prosiding SeNTIK STI&K
Rifki Kosasih
Universitas Gunadarma
Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Neuroscience Nursing Physics Public Health Social Sciences Other

Published : 35 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 35 Documents
Search

 1   2   3   4 
A network-based mobile positioning system using an optimization model Sabri, Ahmad; Kosasih, Rifki
International Journal of Advances in Applied Sciences Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i2.pp298-312

Abstract

The expansion of cellular network coverage facilitates the advancement of research on network-based positioning. We are interested in the signal fingerprinting method to predict the location of a mobile device. By this method, the device must be within the fingerprint coverage to have a successful location prediction. However, any disturbance in the signal propagation would decrease the prediction accuracy. We propose an optimization model based on generalized triangulation combined with a signal fingerprint which is treated more adaptively in responding to any signal disturbance. The triangulation method determines the most likely region where the device is located. The solution provides the estimated longitude and latitude of the device. An illustration of the implementation of the model is presented. The model is assessed using the Indosat cellular network in three distinct testbeds in Indonesia, which are: South Jakarta, a metropolitan area; South Tangerang, a buffer area adjacent to the metropolitan area; and Malang, a city surrounded by rural areas. The most favorable outcome yields an average prediction error of 39.6 m, a maximum error of 197.08 m, a minimum error of 0.05 m, and a standard deviation of error of 39.22 m.
Classification of six banana ripeness levels based on statistical features on machine learning approach Kosasih, Rifki; Sudaryanto, Sudaryanto; Fahrurozi, Achmad
International Journal of Advances in Applied Sciences Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v12.i4.pp317-326

Abstract

Banana plants are often cultivated because they have many benefits. In producing, we need to maintain the quality of bananas by looking at banana ripeness levels before being distributed to markets. The level of banana ripeness is related to marketing reach. If the marketing reach is far, bananas should be harvested when the ripeness level of bananas is still relatively low. A system that can classify the degree of ripeness of bananas can help overcome this problem. In this study, our dataset includes 6 ripeness levels of bananas, more than in previous related studies. Furthermore, we use the statistical features extraction method to find the parameters that affect the level of banana ripeness, considering the texture and color of the banana peel which determines the level of ripeness visually. The extraction used is features extraction based on a histogram, then we employ four features, i.e., mean, skewness, energy descriptor, and smoothness, generated from the image dataset. In the next stage, we perform classification based on the features that have been obtained. In this study, we use Naive Bayes classifier and support vector machine (SVM) algorithms. Based on the result of this research, the best performance is the Naive Bayes classifier, with an accuracy is 86.67%, a weighted average precision of 83.55%, and a weighted average recall of 86.67%.
Perbandingan Kinerja Image Caption Generator Berbasis Pre-Trained CNN Sabri, Ahmad; Kosasih, Rifki
Prosiding Seminar SeNTIK Vol. 9 No. 1 (2025): Prosiding SeNTIK 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

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

Abstract

Penelitian membandingkan lima model CNN (InceptionV3, MobileNetV2, VGG16, VGG19, DenseNet121). Hasilnya, MobileNetV2 memberikan kinerja terbaik berdasarkan metrik BLEU
Penerapan Algoritma Support Vector Machine Dalam Pengenalan Wajah Berdasarkan Fitur Isomap Kosasih, Rifki; Mardhiyah, Iffatul; Indarti, Dina
CESS (Journal of Computer Engineering, System and Science) Vol. 11 No. 1 (2026): Januari 2026
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v11i1.68568

Abstract

Pengenalan wajah merupakan salah satu bidang yang digunakan untuk mengenali seseorang melalui citra ataupun video. Pengenalan wajah ini dapat digunakan untuk absensi kehadiran yang lebih efektif dan efisien dibandingkan dengan absensi menggunakan cara manual. Pada penelitian ini data yang digunakan merupakan data citra wajah yang terdiri dari 6 orang dengan tiap orang memiliki 4 variasi ekspresi wajah. Tahapan selanjutnya adalah melakukan ekstraksi fitur wajah dengan menggunakan metode isomap. Metode isomap adalah salah satu metode yang dapat mereduksi dimensi dari dimensi yang tinggi ke dimensi yang lebih rendah. Dalam studi ini dimensi yang dihasilkan sebanyak 4 sehingga terdapat 4 fitur yang akan digunakan dalam pengklasifikasian wajah. Fitur-fitur tersebut dibagi menjadi fitur latih dan fitur uji. Untuk pengklasifikasian wajah, digunakan metode support vector machine (SVM). Metode support vector machine merupakan metode supervised learning yang dapat digunakan dalam pengenalan pola dan klasifikasi. Metode support vector machine memperhatikan perhitungan jarak kedekatan fitur satu dengan fitur lainnya dalam pengenalan pola dan klasifikasi. Berdasarkan hasil klasifikasi diperoleh tingkat akurasi sebesar 87,5%, rata-rata terbobot presisi sebesar 79,1675% dan rata-rata terbobot recall sebesar 87,5%.   
Optimalisasi Deteksi Tingkat Kematangan Tanda Buah Segar Kelapa Sawit Menggunakan YOLOV8 Dengan Platform Web Mardhiyah, Iffatul; Sari, Dyan Prawita; Genoveva, Zahwa; Kosasih, Rifki; Irawati, Dyah Cita
Jurnal Ilmiah Teknologi dan Rekayasa Vol. 30 No. 3 (2025)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/tr.2025.v30i3.67

Abstract

Oil palm represents one of Indonesia’s principal commodities. Traditionally, farmers manually monitor the ripeness level of palm oil, but this method is neither effective nor efficient for large-scale harvests. Therefore, a system that can automatically detect the ripeness level of fresh fruit bunches (FFB) is needed. In this study, the YOLOv8 algorithm was used which was integrated into a web-based application. The system is designed to improve accuracy and efficiency in the grading process of oil palm fruits, which directly impacts the quality of processed products and palm oil production. The dataset used consists of 6.592 images obtained through the Roboflow platform, covering various ripeness categories. The system development follows the CRISP-DM approach, consisting of business understanding, data understanding, data preparation, modeling, evaluation and deployment. The model training process approximately 3,1 hours, with evaluation results showing a precision of 94,5%, recall of 94,7%, and a mean Average Precision (mAP) of 98%. The model’s performance is further supported by an F1-confidence curve of 95% and a precision-recall curve of 98%, indicating stable and accurate classification capabilities. The model is deployed through a Streamlit-based web interface, allowing users to perform real-time detection from images or videos without requiring additional installations.
Co-Authors Achmad Fahrurozi Ahmad Sabri Aini Suri Talita Anggi Alberto Christian Daomara Desti Riminarsih Dewi Agushinta R. Dewi Putrie Lestari Didin Mukodim Dina Indarti Dyah Cita Irawati Dyan Prawita Sari Eko Sri Margianti Fadlia, Nur Fahrurozi, Achmad Fazriyati, Elyna Genoveva, Zahwa Hendri Dwi Putra Hidayat Taufik Akbar Hutapea, Octaviani Iffatul Mardhiyah Ilmiyati Sari Ilmiyati Sari Irawati, Dyah Cita Mardhiyah, Iffatul Muhammad Arfiansyah Nurabsharina, Amanda Putri Nurma Nugraha Rahayu, Dicka Ariptian Revanza Raditya Putra Yanni Sari, Dyan Prawita Sarifuddin Madenda SUDARYANTO SUDARYANTO Suryadi Harmanto