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Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
Published by Universitas Muhammadiyah Malang
ISSN : 25032259     EISSN : 25032267     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control was published by Universitas Muhammadiyah Malang. journal is open access journal in the field of Informatics and Electrical Engineering. This journal is available for researchers who want to improve their knowledge in those particular areas and intended to spread the knowledge as the result of studies. KINETIK journal is a scientific research journal for Informatics and Electrical Engineering. It is open for anyone who desire to develop knowledge based on qualified research in any field. Submitted papers are evaluated by anonymous referees by double-blind peer review for contribution, originality, relevance, and presentation. The Editor shall inform you of the results of the review as soon as possible, hopefully within 4 - 8 weeks. The research article submitted to this online journal will be peer-reviewed at least 2 (two) reviewers. The accepted research articles will be available online following the journal peer-reviewing process.
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Articles 10 Documents
 1 
Search results for , issue "Vol. 7, No. 3, August 2022" : 10 Documents clear
Mobile Device Security Evaluation using Reverse TCP Method Riadi, Imam; Sunardi; Aprilliansyah, Deco
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 7, No. 3, August 2022
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v7i3.1433

Abstract

Security evaluation on Android devices is critical so that users of the operating system are protected from malware attacks such as remote access trojans that can steal users' credential data. Remote access trojan (RAT) attacks can be anticipated by detecting vulnerabilities in applications and systems. This study simulates a remote access trojan attack by exploiting it until the Attacker gains full access to the victim's device. The episode is carried out with several steps: creating a payload, installing applications to the victim's device, connecting listeners, and performing exploits to retrieve important information on the victim's device. Test material using Android 12, problems occurred when trying to install the application because a harmful warning will appear from Play Protect due to not using the latest version of privacy protection which causes the application to be indicated as malware and the like. On Android 11, the application injected with the backdoor was successfully installed on the device and successfully accessed by the attacker. Attackers also get vital information, including system information, contacts, call logs, messages, and full access to the victim's device system directory. Based on this research, it is expected that Android device users constantly update the Android version on the device they are using.
Feature Selection Based on Multi-Filters for Classification of Mammogram Images to Look for Signs of Breast Cancer ‘Uyun, Shofwatul
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 7, No. 3, August 2022
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v7i3.1437

Abstract

The accuracy of classification results on mammogram images has a significant role in breast cancer diagnosis. Therefore, many stages consider finding the model has a high level of accuracy and minimizing the computing load, one of which is the accuracy in using the best feature. This needs to be prioritized considering that mammogram image has many features resulting from the mammogram extraction process. Our research has four stages: feature extraction, feature selection-multi filters, classification, and performance evaluation. Thus, in this research, we propose algorithms that can select the features by utilizing multiple filters simultaneously on the filter model for feature selection of mammogram images based on multi-filters/FSbMF. There are six feature selection algorithms with a filter approach (information gain, rule, relief, correlation, gini index, and chi-square) used in this research. Based on the testing result using 10-fold cross-validation, the features resulting from the FSbMF algorithm have the best performance based on the accuracy, recall, and precision from 72,63%, 70,38%, 75,01% to be 100%. Furthermore, the number of resulting features is the minimum because it results from intersection operation from the feature subsets resulting from the multi-filter.
A Modified Maximum Power Point Tracking with Constant Power Generation Using Adaptive Neuro-Fuzzy Inference System Algorithm Sudiharto, Indhana; Prasetyono, Eka; Budikarso, Anang; Fitria Devi, Safira
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 7, No. 3, August 2022
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v7i3.1452

Abstract

Renewable energy is being used to lessen the consumption of fossil fuels. Solar energy is a common source of renewable energy. Solar energy is the most promising source of energy due to its long-term sustainability and availability. The output power of solar panels is strongly influenced by the intensity of sunlight and the temperature of the solar panels. Maximum Power Point Tracking (MPPT) control, which aims to optimize the output power of solar panels, is commonly used to increase the efficiency of solar panels. However, MPPT control often causes overvoltage disturbance in systems directly connected to the load. To limit the output power of solar panels, additional Constant Power Generation (CPG) control is required. In this research, a solar panel system will be created to supply submersible DC pumps without any energy storage devices. DC-DC SEPIC Converter is designed with MPPT control combined with CPG control to limit the output power of the converter using the Adaptive Neuro-Fuzzy Inference System method by 150 watts. When the output power of the solar panel is less than the power limit, then MPPT mode will work. While CPG mode works when the PV output power is greater than the limit power. The results of this research showed that the system can provide optimal power generated by solar panels in MPPT mode by increasing efficiency by up to 33.04% and CPG mode can limit power to 150 Watts to avoid overvoltage disturbance at load.
High Accuracy Electric Water Heater using Adaptive Neuro-Fuzzy Inference System (ANFIS) Sudiharto, Indhana; Dwi Murdianto, Farid; Budikarso, Anang; Taufika, Putri
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 7, No. 3, August 2022
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v7i3.1453

Abstract

Nowadays, water heater is a common household appliance. Water heater can be divided into three types, based on fuel sources: gas, diesel, and electric. Electric water heater is the most common due to its ease of use. The problems that often occur on electric water heater are over-temperature due to user error in setting up the thermostat and inaccurate readings caused by a conventional system control. These problems will cause a surge in power consumption. Over-temperature and conventional control inaccuracies can be overcome using the Artificial Intelligence (AI) control algorithm in the form of an adaptive neuro-fuzzy inference system (ANFIS). The proposed algorithm acts as a control by maintaining the stability of the temperature to obtain more accurate results. An accurate temperature reading can lower power consumption in electric water heater. This study tries to simulate Electric Water Heater temperature control using the ANFIS algorithm until stable readings can be achieved in all temperature settings. Results from disturbance tests in the form of external condition that causes sudden temperature change show that the system can maintain stability with an average error margin of 0.045% and the rate of accuracy of 99.955%.
Fuzzy Type-2 Trapezoid Methods for Decision Making Salt Farmer Mapping Kustiyahningsih, Yeni; Rahmanita, Eza; Purbandini, Purbandini; Purnama, Jaka
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 7, No. 3, August 2022
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v7i3.1454

Abstract

The need for domestic salt every year has increased, both for consumption and industrial salt. Some of the fisheries service programs include providing assistance to people's businesses, providing geomembrane, and online marketing training. A large number of salt farmers and official work programs have caused the implementation of the program to be less than optimal, resulting in low salt production. This study uses a type-2 fuzzy method by integrating two methods, namely type-2 Fuzzy Analytical Hierarchy Process AHP (FAHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Fuzzy type-2 has higher accuracy than fuzzy type-1 and is more efficient and more flexible in determining the linguistic scale for criteria. The Fuzzy Analytical Hierarchy Process AHP (FAHP) interval is used to determine the weight of the salt farmer mapping criteria. Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS), used to determine. The findings of this study are that the indicators that most influence the mapping of salt farmers are land area, marketing, and market. The results of the mapping of salt farmers are the classification of salt farmer class groups and recommendations for improvement for each salt farmer. Hybrid type-2 Fuzzy Analytical Hierarchy Process AHP (FAHP) method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), can be used for mapping salt farmers based on the consistency ratio value below 10 percent, 37 percent enter high class, 28 percent enter the middle class and 35 percent enter low class
Mass Classification of Breast Cancer Using CNN and Faster R-CNN Model Comparison Sunardi, Sunardi; Yudhana, Anton; Windra Putri, Anggi Rizky
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 7, No. 3, August 2022
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v7i3.1462

Abstract

Threat of breast cancer is a frightening type and threatens the female population worldwide. Early detection is preventive solution to determine cancer diagnosis or tumors in the female breast area. Today, machine learning technology in managing medical images has become an innovative trend in the health sector. This technology can accelerate diagnosing disease based on the acquisition of accuracy values. The primary purpose of this research is to innovate by comparing two deep learning models to build a prediction system for early-stage breast cancer. This research utilizes Convolutional Neural Network (CNN) sequential models and Faster Region-based Convolutional Neural Network (R-CNN) models that can determine the classification of normal or abnormal breast image data, which can determine the normal or abnormal classification of breast image. The dataset's source in this study came from the Mammographic Image Analysis Society (MIAS). This dataset consists of 322 mammogram data with 123 abnormal and 199 normal classes. The experimental results of this study show that the accuracy of the CNN and R-CNN models in image classification are 91.26% and 63.89%, respectively. Based on these results, the CNN sequential model has better accuracy than the Faster R-CNN model, because it does not require unique characteristics to detect breast cancer.
Segmentation of Facial Bones from Skull Point Clouds Based on Smoothed Deviation Angle Ulinuha, Masy Ari; Yuniarno, Eko Mulyanto; Purnama, I Ketut Eddy; Hariadi, Mochamad
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 7, No. 3, August 2022
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v7i3.1464

Abstract

The human skull was the subject of study in various fields. Segmentation could be a basic tool for better understanding the skull. One of the most challenging tasks was facial bone segmentation. Our previous study had succeeded in segmenting facial bones from skull point clouds, however the quality of the results needed to be improved. In this paper, we proposed a new method to improve the results of facial bone segmentation from skull point clouds. The method consists of three stages: deviation angle extraction, smoothing, and thresholding. Each point in the point cloud was assigned a value based on the deviation angle. These values then went through a smoothing process to clarify the differences between the facial bone region and other regions. Next, thresholding was performed to divide the skull into two regions, namely facial bone and non-facial bone. The proposed method had succeeded in improving the quality of the segmentation results by achieving precision=0.931, recall=0.9854, and F=0.9573.
Light-Fidelity as Next Generation Network Technology: A Bibliometric Survey and Analysis Safitri, Cutifa; Galina, Mia; Simatupang, Joni Welman
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 7, No. 3, August 2022
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v7i3.1469

Abstract

This paper delivers a systematic review and a bibliometric survey analysis of Light-Fidelity (Li-Fi) indoor implementation in Next Generation Network (NGN). The main objective of this study is to design a communication network based on NGN-Li-Fi for the indoor implementation which aims to increase user Quality of Service (QoS). The main merits and contributions of this study are the thorough and detailed analysis of the review, both in literature surveys and bibliometric analysis, as well as the discussion of the implementation model challenges of Li-Fi in both indoor and outdoor environments. The issue articulated in an indoor communication network is the possibility of intermittent connectivity due to barriers caused by line-of-sight (LOS) between the LED transmitter and receiver, handover due to channel overlap, and other network reliability issues. To realize the full potential and significant benefits of the Next Generation Network, challenges in indoor communication such as load-balancing and anticipating network congestion (traffic congestion) must be addressed. The main benefit of this study is the in-depth investigation of surveys in both selected critical literatures and bibliometric approach. This study seeks to comprehend the implications of Next Generation networks for indoor communication networks, particularly for visible light communication channels.
KNN Algorithm for Identification of Tomato Disease Based on Image Segmentation Using Enhanced K-Means Clustering Nasution, Amir Saleh; Alvin, Alvin; Siregar, Ana Tince; Sinaga, Monica Sari
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 7, No. 3, August 2022
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v7i3.1486

Abstract

Image segmentation is an important process in identifying tomato diseases. The technique that is often used in this segmentation is k-means clustering. One of the main problems in this technique is the case of local minima, where the cluster that is formed is not suitable due to the incorrect selection of the initial centroid. In image data, this case will have an impact on poor segmentation results because it can erase parts that are actually important to be lost or there is still background in the recognition process, which has an impact on decreasing accuracy results. In this research, a method for image segmentation will be proposed using the k-means clustering algorithm, which has been added with the cosine similarity method as the proposed contribution. The use of the cosine method will determine the initial centroid by calculating the level of similarity of each image feature based on color and dividing them into several categories (low, medium, and high values). Based on the results obtained, the proposed algorithm is able to segment and distinguish between leaf and background images with good results, with the kNN reaching a value of 94.90% for accuracy, 99.50% for sensitivity, and 93.75% for specificity. The results obtained using the kNN method with k-means segmentation obtained a value of 92.46% for accuracy, 96.30% for sensitivity, and 91.50% for specificity. The results obtained using the kNN method without segmentation obtained a value of 90.22% for accuracy, 93.30% for sensitivity, and 89.45% for specificity.
Javanese Character Recognition Based on K-Nearest Neighbor and Linear Binary Pattern Features Susanto, Ajib; Mulyono, Ibnu Utomo Wahyu; Sari, Christy Atika; Rachmawanto, Eko Hari; Ali, Rabei Raad
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 7, No. 3, August 2022
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v7i3.1491

Abstract

Javanese script (Hanacaraka) is one of the cultures owned by Indonesia. Javanese script is found in temples, inscriptions, cultural and prehistoric sites, ancient Javanese manuscripts, Gulden series banknotes, street signage, and palace documents. Javanese script has a form with an article, and the use of reading above the script is a factor that affects the character detection process. Punctuation marks, clothing, Swara script, vowels, and consonants are parts of the script that are often found in Javanetest scripts. Preserving Javanese script in the digital era, of course, must use technology that can support the digitization of Javanese script through the script detection process. The concept of script image is the image of Javanese script in ancient manuscripts. The process of character detection using certain techniques can be carried out to extract characters so that they can be read. Detection of Javanese characters can be found by finding a testing image. Here, we had been used 10 words images consisting of 3 to 5 syllables with the vowel aiu. Dataset process by Linear Binary Pattern (LBP) feature extraction, which is used to characterize images and describe image textures locally. LBP has been used in r=4 and preprocessing is also done by thresholding with d=0.3. This process can be done using the K-Nearest Neighbor algorithm. In 10 datasets of Javanese script words, an average accuracy value of 90.5% was obtained. The accuracy value of 100% is the highest and 50% is the lowest.

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