Article Per Year (5 Year)
p-Index From 2020 - 2025
2.978
P-Index
This Author published in this journals
All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) Register: Jurnal Ilmiah Teknologi Sistem Informasi JOIN (Jurnal Online Informatika) Muaddib : Studi Kependidikan dan Keislaman Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JURNAL INSTEK (Informatika Sains dan Teknologi) Literasi: Jurnal Ilmiah Pendidikan Bahasa, Sastra Indonesia dan Daerah JOISIE (Journal Of Information Systems And Informatics Engineering) Jurnal Kajian Ilmiah Paedagogie: Jurnal Pendidikan dan studi ISlam TAJDID: Jurnal Pemikiran Keislaman dan Kemanusiaan Indonesian Journal of EFL and Linguistics Kohesi: Jurnal Sains dan Teknologi Jurnal Penelitian Pendidikan Indonesia
Sari, Zamah
Unknown Affiliation
Religion Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Social Sciences Other

Published : 23 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 7 Documents
Search
Journal : Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control

 1 
Performance Comparisson Human Activity Recognition Using Simple Linear Method Kusuma, Wahyu Andhyka; Sari, Zamah; Minarno, Agus Eko; Wibowo, Hardianto; Akbi, Denar Regata; Jawas, Naser
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 5, No. 1, February 2020
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (731 KB) | DOI: 10.22219/kinetik.v5i1.1025

Abstract

Human activity recognition (HAR) with daily activities have become leading problems in human physical analysis. HAR with wide application in several areas of human physical analysis were increased along with several machine learning methods. This topic such as fall detection, medical rehabilitation or other smart appliance in physical analysis application has increase degree of life. Smart wearable devices with inertial sensor accelerometer and gyroscope were popular sensor for physical analysis. The previous research used this sensor with a various position in the human body part. Activities can classify in three class, static activity (SA), transition activity (TA), and dynamic activity (DA). Activity from complexity in activities can be separated in low and high complexity based on daily activity. Daily activity pattern has the same shape and patterns with gathering sensor. Dataset used in this paper have acquired from 30 volunteers.  Seven basic machine learning algorithm Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, Gradient Boosted and K-Nearest Neighbor. Confusion activities were solved with a simple linear method. The purposed method Logistic Regression achieves 98% accuracy same as SVM with linear kernel, with same result hyperparameter tuning for both methods have the same accuracy. LR and SVC its better used in SA and DA without TA in each recognizing.
QoS Analysis Of Kinematic Effects For Bluetooth HC-05 And NRF24L01 Communication Modules On WBAN System Faiqurahman, Mahar; Novitasari, Diyan Anggraini; Sari, Zamah
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol 4, No 2, May 2019
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v4i2.826

Abstract

Wireless Body Area Network (WBAN) consists of a number of sensor nodes that are attached to the human body, and intended for monitor the human body condition. The WBAN system has several wireless communication modules that are used for sending or exchanging data between sensor nodes and gateway nodes or gateway nodes. There are some factors that are used to decide which communication modules should be implemented on WBAN system, including communication efficiency, distance range, power consumption, and the effect of mobility on QoS. In this study, we analyze the impact of the kinematic movement of sensor nodes on QoS parameter of HC-05 Bluetooth and NRF25L01 communication modules, during sending and receiving process among nodes. We assume that the sensor node and gateway node are attached on the limbs to catch the movement. We use Quality of Service (QoS) parameters such as delay, jitter, and packet loss, to analyze the impact of movement on communication modules. Based on the experimental result, it was found that the average value of delay and jitter for booth communication modules was slightly influenced by the speed of the sensor node movement. During the sensor node movement and data transmission, we found that the NRF24L01 module have a lower delay and jitter value than Bluetooth HC-05 module. The percentage of packet loss tends to be stable at 0% value, even though the speed value becomes higher.
Implementation of Generative Adversarial Network (GAN) Method for Pneumonia Dataset Augmentation Chandranegara, Didih Rizki; Sari, Zamah; Dewantoro, Muhammad Bagas; Wibowo, Hardianto; Suharso, Wildan
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 8, No. 2, May 2023
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v8i2.1675

Abstract

As a communicable disease, the majority of pneumonia cases are brought on by bacteria or viruses, which cause the lungs' alveoli to swell with fluid or mucus. Pneumonia may arise from this and further making breathing challenging since the lungs' air sacs are unable to contain enough oxygen for the body. Pneumonia may generally be diagnosed clinically (by a physician based on physical symptoms) as well as through a photo chest radiograph, CT scan, and MRI. In this case, the lower cost of a chest radiograph examination making it as one of the most popular medical imaging tests. However, chest radiograph photo readings have a disadvantage, where it takes a long time for medical staff or physicians to identify the patient's illness since it is difficult to detect the condition. Therefore, an identification of chest radiograph imagery into various forms using machine learning becomes one way to address this issue. This research focuses on building a deep neural network model using techniques from the Generative Adversarial Network algorithm. GAN is a category of machine learning techniques using two models to be trained simultaneously, one is a generator model to generated fake data and the other is a discriminator model used to separate the raw data from the real data set images. The dataset used is Chest X-Ray images obtained from repo GitHub and repo Kaggle totaling 5,863 with normal data 1583 images and pneumonia data 4273 imagesThe results showed that the use of the Generative Adevrsarial Network method as augmentation data proved to be more effective in improving the generalization of neural networks, this can be seen from the results the result of the accuracy value obtained is 97%.
Classification of Coffee Leaf Diseases using CNN Sucia, Dara; Shintya Larasabi , Auliya Tara; Azhar, Yufis; Sari, Zamah
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 8, No. 3, August 2023
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v8i3.1745

Abstract

Indonesia’s coffee industry plays a crucial role as a major export, making a significant contribution to the country’s economy by generating foreign exchange. The quality and quantity of coffee production depend on various factors such as humidity, rain, and fungus that can cause rust diseases on coffee leaves. These diseases can spread quickly and affect other coffee plants quality, leading to decreased production. To address this issue, CNN with VGG-19 architecture model was utilized to identify coffee plant diseases using image data and the python programming language, which in previous studies used MATLAB as their platform. In addition, VGG-19 with image enhancement and contouring data for pre-processing step has a more profound learning feature than the method used in the previous studies, AlexNet which makes the structure of VGG- 19 more detailed. The dataset used in this paper is Robusta Coffee Leaf Images Dataset which have three classes, namely health, red spider mite, and rust. The VGG-19 model attained F1-Score of 90% when evaluated using the testing data with ratio 80:20, where 80% is training data, and 20% is validation data. This paper employed 0.0001 learning rate, batch size 15, momentum 0.9, 12 training iteration, and RMSprop optimizer.
Optimizing Android Program Malware Classification Using GridSearchCV Optimized Random Forest Hakim, Luqman; Sari, Zamah; Aristyo, Ananda Rizaldy; Pangestu, Syahrul
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 9, No. 2, May 2024
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v9i2.1944

Abstract

The growing number of smartphones, particularly Android powered ones, has increased public awareness of the security concerns posed by malware and viruses. While machine learning models have been studied for malware prediction in this field, methods for precise identification and classification still require improvement for the perfect detection of malwares and minimizing the cracks on machine learning based classification. Detection accuracy that ranges from 93% to 95% has been observed in prior research, indicates room for improvement.  In order to maximize the hyperparameters, this paper suggests improving the Random Forest method by introducing the grid search algorithm which isn’t present in previous studies. A significant increase in classification accuracy is the main aim of the research. We exhibit an outstanding 99% accuracy rate in detecting malware contaminated programs, demonstrating the significance of our technique. The proposed method can be seen as a huge improvement over existing models, achieving near perfection in detection, in contrast to which typically obtained by previous models with the accuracy rate of 95% max on the same dataset. Our approach achieves such high accuracy and provides a novel remedy for the limits of the Android based platforms, particularly when program processing resources are limited. This study confirms the effectiveness of our improved Random Forest algorithm, points to a paradigm shift in malware detection, and heightened cybersecurity measures for the rapidly growing smartphone market.
Predicting the Sentiment of Review Aspects in the Peer Review Text using Machine Learning Basuki, Setio; Sari, Zamah; Tsuchiya, Masatoshi; Indrabayu, Rizky
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 9, No. 4, November 2024
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v9i4.2042

Abstract

This paper develops a Machine Learning (ML) model to classify the sentiment of review aspects in the peer review text. Reviewers use the review aspect as paper quality indicators such as motivation, originality, clarity, soundness, substance, replicability, meaningful comparison, and summary during the review process. The proposed model addresses the critique of the existing peer review process, including a high volume of submitted papers, limited reviewers, and reviewer bias. This paper uses citation functions, representing the author's motivation to cite previous research, as the main predictor. Specifically, the predictor comprises citing sentence features representing the scheme of citation functions, regular sentence features representing the scheme of citation functions for non-citation sentences, and reference-based representing the source of citation. This paper utilizes the paper dataset from the International Conference on Learning Representations (ICLR) 2017-2020, which includes sentiment values (positive or negative) for all review aspects. Our experiment on combining XGBoost, oversampling, and hyper-parameter optimization revealed that not all review aspects can be effectively estimated by the ML model. The highest results were achieved when predicting Replicability sentiment with 97.74% accuracy. It also demonstrated accuracies of 94.03% for Motivation and 93.93% for Meaningful Comparison. However, the model exhibited lower effectiveness on Originality and Substance (85.21% and 79.94%) and performed less effectively on Clarity and Soundness with accuracies of 61.22% and 61.11%, respectively. The combination predictor was the best for the 5 review aspects, while the other 2 aspects were effectively estimated by regular sentence and reference-based predictors.
Transfer Learning Approaches for Non-Organic Waste Classification: Experiments with MobileNet and VGG-16 Sari, Zamah; Basuki, Setio
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 4, November 2025 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i4.2319

Abstract

This research aims to develop machine learning (ML) models for classifying non-organic waste. The goal is to support more effective waste management by increasing recycling rates, reducing landfill use, and minimizing environmental impact. The ML models proposed in this paper classify 20 types of non-organic waste collected from the internet, which consists of 2,552 instances. Experiments on our dataset reveal key findings. First, MobileNet, achieving 86% accuracy, outperforms VGG-16, which reaches 72% accuracy. Second, both models effectively classify distinct objects such as cigarette butts, toothbrushes, and glass bottles, demonstrating strong pattern recognition for these categories. Third, both models struggle with misclassification in visually similar categories, particularly paper-based waste like cardboard, carton packaging, books, and foam packaging. Fourth, MobileNet shows notable confusion in classifying plastic packaging, carton packaging, and books, while VGG-16 exhibits greater misclassification in foam packaging, cardboard, and newspapers. These results highlight the challenge of distinguishing waste types with overlapping textures and shapes. Moreover, it presents the urgency of improving the model to distinguish visually similar waste materials. Looking at the number of labels used in this paper compared with existing studies, the findings demonstrate the competitiveness of our models for non-organic waste classification.
Co-Authors Abdul Hakim, Sudarnoto Abdul Munir Mulkhan Adian Husaini Agus Eko Minarno Akbi, Denar Regata Amirullah -, Amirullah Anam, Bagas Khoirul Aristyo, Ananda Rizaldy Bashor Fauzan Muthohirin Basuki, Setio Chandranegara, Didih Rizki Dewantoro, Muhammad Bagas Didin Saefudin Dwi Fajri, Muhamad Dwifajri, Muhammad Hakim , Sudarnoto Abdul Hardianto Wibowo Hidayatullah, Rakhmad Arif Ina Setiawati Indrabayu, Rizky Jupri, Abdul Rahman Kiki, Muhammad Rizkiansyah Maulana Kusumawardani, Mila Kusumawati, Endah Tri Luqman Hakim Mahar Faiqurahman MANGUNWIBAWA, ARIA AHMAD Moechammad Sarosa Mulya, Gani Mulyono, Herdiana Muthohirim, Bashor Fauzan Nani Solihati Naser Jawas, Naser Novitasari, Diyan Anggraini Nurhanan, Nurhanan Nurjam'an, Muhamad Ichsan Pangestu, Syahrul Pratama, Bagus Aryo Hany Primandhika, Restu Bias Rizki, Didih Saefuddin, Didin Salam, Fajar Mayda Shintya Larasabi , Auliya Tara Sucia, Dara Sudarnoto Abdul Hakim Suyono, Achmad Syahid Widyanto, Azis Nur Syamsul Arifin Tsuchiya, Masatoshi Wahyu Andhyka Kusuma Wildan Suharso Yufis Azhar