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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

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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

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Abstract

Penelitian membandingkan lima model CNN (InceptionV3, MobileNetV2, VGG16, VGG19, DenseNet121). Hasilnya, MobileNetV2 memberikan kinerja terbaik berdasarkan metrik BLEU
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 Hendri Dwi Putra Hidayat Taufik Akbar Hutapea, Octaviani Iffatul Mardhiyah Ilmiyati Sari Ilmiyati Sari Mardhiyah, Iffatul Muhammad Arfiansyah Nurabsharina, Amanda Putri Nurma Nugraha Rahayu, Dicka Ariptian Revanza Raditya Putra Yanni Sarifuddin Madenda SUDARYANTO SUDARYANTO Suryadi Harmanto