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All Journal Telematika JURNAL PENGABDIAN KEPADA MASYARAKAT JOIN (Jurnal Online Informatika) Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Jurnal Mahasiswa Bina Insani Informatics for Educators and Professional : Journal of Informatics Faktor Exacta Jurnal SOLMA JUTIK : Jurnal Teknologi Informasi dan Komputer JOURNAL OF APPLIED INFORMATICS AND COMPUTING CARADDE: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknologi Informasi dan Multimedia Aisyah Journal of Informatics and Electrical Engineering Respati Scientific Journal of Informatics Infotech: Journal of Technology Information Teknomatika (Jurnal Teknologi dan Informatika) Journal Of Information And Technology Unimor (JITU) Journal of Informatics and Communication Technology (JICT) Jurnal Pengabdian Mitra Masyarakat (JPMM) J-ABDIPAMAS (Jurnal Pengabdian Kepada Masyarakat) Jikom: Jurnal Informatika dan Komputer Jurnal INFOTEL Journal of Intelligent Systems and Information Technology Journal of Informatics and Communication Technology (JICT) Jurnal Pengabdian Masyarakat (JURDIMAS) Sistematis JUTEKOM - Jurnal Teknologi dan Ilmu Komputer
Chusyairi, Ahmad
Universitas Siber Asia
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Medicine & Pharmacology Social Sciences Other

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Implementasi Authentication Captive Portal Pada Wireless Local Area Network di PT. St. Morita Industries Siregar, Jonathan Daniel; Chusyairi, Ahmad
Jurnal Informatika dan Komputer Vol 14 No 1 (2024): April
Publisher : Sekolah Tinggi Ilmu Komputer PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55794/jikom.v14i1.119

Abstract

PT. St. Morita Industries has a Wireless Local Area Network (WLAN) which is used as a medium for exchanging data and information by utilizing wireless transmission media, WLAN currently uses WPA2-PSK as a security system to authenticate users to access the internet. However, the use of WPA2-PSK as WLAN security still has a weakness due to the use of the same 1 password for many users in order to connect to the WLAN hotspot will be an opportunity for cyber crime. This happens because it will be very easy for irresponsible users to enter the WLAN. Therefore, in this study, captive portal authentication will be applied as an effort to increase WLAN security replaces WPA2-PSK. This research process uses the Network Development Life Cycle (NDLC) method. All the configurations needed to build the captive portal authentication take advantage of the Winbox program. This research has resulted in a special user authentication limitation for users who have registered on the WLAN is permitted to access this company's internet. In addition, the Winbox program can also be used for monitoring all users connected to the WLAN, both active and inactive users. By setting up captive portal authentication on a WLAN network using a proxy routerboard device which comprises an IP address, DHCP server, hostpot setup, NAT firewall, and DNS server one can create a WPA2-PSK security system. This configuration is completed with the aid of the Winbox v3.37 software.
Clustering Data Cuaca Ekstrim Indonesia dengan K-Means dan Entropi Chusyairi, Ahmad
Journal of Informatics and Communication Technology (JICT) Vol. 5 No. 1
Publisher : PPM Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52661/j_ict.v5i1.146

Abstract

As information technology develops in agriculture, more patterns of database systems are manual or computerized. However, the amount of data available does not always match the knowledge that can be generated. The community really needs weather information regardless of the format if it is reliable and valid. One is to determine local climate patterns. The data collection method uses a document study method, then uses data analysis techniques with a scoring system on rainfall data, maximum temperature, minimum temperature, average temperature, humidity, and wind speed. The K-Means Clustering method is a technique for grouping data. From the analysis carried out, there are 3 classes to cluster the weather levels produced by the entropy test to avoid bias towards non-optimal precision and accuracy. The division of the number of clusters can be captured as a type of potential vulnerability, namely high, medium, and mild where the total of all weather data from all BMKG stations throughout Indonesia is 123 data, 52 of which are classified as areas that have the potential to experience extreme high weather, 31 are classified as areas that experience extreme weather, moderate extreme weather potential, and 40 areas classified as areas with mild extreme weather potential.
PROTOTYPE ALAT JEMURAN PAKAIAN OTOMATIS MENGGUNAKAN ARDUINO BERBASIS ANDROID Azis, Afif; Chusyairi, Ahmad
Infotech: Journal of Technology Information Vol 7, No 2 (2021): NOVEMBER
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v7i2.112

Abstract

The clotheslines used by the community are still in manual form so people still have to lift the clothesline directly. People who have a lot of interests or who work will not have time to lift the clothesline directly so they have to leave their more important work. People are still confused about how to lift clotheslines with the uncertain weather when there are other jobs or traveling. Based on these problems, a prototype model of an automatic clothesline was built using an Android-based Arduino Uno, this is to lighten and shorten the time in lifting clotheslines or drying clothes when the weather is changing. The purpose of this research is to make a tool that can help reduce household chores, especially drying clothes automatically using LDR sensors and rain sensors and can be controlled by cellphones. An automatic clothesline tool has been designed using an Android-based Arduino. In making the prototype using the LDR sensor as a light detector, using a water/rain sensor as a rainwater detector and using a servo motor to open and close the clothesline roof, and use the HC-05 Bluetooth module to move the clothesline roof with a cellphone using bluetooth which is controlled with an Arduino microcontroller. UNO which functions as a data processing center. After testing this tool works well, the sensor will check the weather outside whether it is sunny or rainy. When the weather is sunny or hot outside, the roof of the clothesline will automatically open and if it is raining outside, the roof of the clothesline will automatically close. When the water sensor and LDR sensor do not work or experience problems, the automatic clothesline can be controlled with a smartphone that is connected to the HC-05 Bluetooth module. The result of this research is that the automatic clothes drying device using Arduino Uno can ease household chores when drying clothes and based on the test results with the blackbox table the tool runs 100% as desired.
Machine Learning Monitoring Model for Fertilization and Irrigation to Support Sustainable Cassava Production: Systematic Literature Review Chusyairi, Ahmad; Herdiyeni, Yeni; Sukoco, Heru; Santosa, Edi
JOIN (Jurnal Online Informatika) Vol 9 No 2 (2024)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v9i2.1328

Abstract

The manual and time-consuming nature of current agronomic technology monitoring of fertilizer and irrigation requirements, the possibility of overusing fertilizer and water, the size of cassava plantations, and the scarcity of human resources are among its drawbacks. Efforts to increase the yield of cassava plants > 40 tons per ha include monitoring fertilization approach or treatment, as well as water stress or drought using UAVs and deep learning. The novel aspect of this research is the creation of a monitoring model for the irrigation and fertilizer to support sustainable cassava production. This study emphasizes the use of Unnamed Aerial Vehicle (UAV) imagery for evaluating the irrigation and fertilization status of cassava crops. The UAV is processed by building an orthomosaic, labeling, extracting features, and Convolutional Neural Network (CNN) modeling. The outcomes are then analyzed to determine the requirements for air pressure and fertilization. Important new information on the application of UAV technology, multispectral imaging, thermal imaging, among the vegetation indices are the Soil-Adjusted Vegetation Index (SAVI), Leaf Color Index (LCI), Leaf Area Index (LAI), Normalized Difference Water Index (NDWI), Normalized Difference Red Edge Index (NDRE), and Green Normalized Difference Vegetation Index (GNDVI).
Detection and Mitigation of DoS Attacks Based on Decision Tree Algorithm on Log Server Pradana, Ferry; Chusyairi, Ahmad
Journal of Intelligent Systems and Information Technology Vol. 2 No. 2 (2025): July
Publisher : Apik Cahaya Ilmu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61971/jisit.v2i2.149

Abstract

Denial of service (DoS) is an attack on a computer or server on an internet network that consumes computer resources until it can no longer perform its duties properly. The research objective is to develop a DoS attack detection and mitigation system based on the decision tree algorithm on server log analysis. The security method uses the decision tree algorithm because it has classification capabilities and produces simple classification tree decision rules. The system will monitor the spike of an IP in the server log to detect attacks and provide handling with IP Blocking techniques that are able to block the attacker's IP request for a certain duration. Python is used to study the data by generating a rule-based classifier then applied to the system using the PHP programming language and a separate PowerShell implementation so that it can run the system automatically. The database used is MySQL which consists of 2 tables, namely the request log table to store logs of requests that enter the server and ips throttle to store IPs that indicate attacks. The simulation results are the TPR accuracy value of 99.49% while the FPR error value is 0.14%, besides that the system successfully blocked 657 attacks but there were 135 incoming attacks and 17 normal requests were blocked. As a result, the system can predict attacks accurately and block the majority of incoming attacks although it still needs to be further optimised.
Enhancing E-Commerce Customer Segmentation with Fuzzy C-Means Soft Clustering Probabilities Putra, Muhamad Iqbal Januadi; Alexander, Vincent; Chusyairi, Ahmad; Abdurrahman, Raka Admiral; Pratama, Alexander Daniel
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10652

Abstract

Customer segmentation is of paramount importance in the e-commerce industry, enabling businesses to improve marketing strategies and customer engagement. This study compares the performance of two clustering algorithms, K-Means and Fuzzy C-Means (FCM), using Walmart’s public e-commerce dataset of 550,068 transactions. After preprocessing and normalization, the elbow method was applied to determine the optimal number of clusters, yielding seven clusters for K-Means and eight for FCM. Experimental evaluation based on the silhouette score shows that FCM achieved 0.48, outperforming K-Means which scored 0.36, indicating that FCM generated clusters with stronger cohesion and separation. However, this improvement comes at a computational cost. K-Means consistently required less than 0.02 seconds per run, while FCM averaged 0.3 seconds and peaked at 1.38 seconds when the number of clusters increased, making it approximately 20–30 times slower. Cluster distribution analysis further revealed that K-Means produced an uneven segmentation dominated by a single large cluster, whereas FCM generated a more balanced distribution across its clusters. This demonstrates the advantage of FCM in capturing overlapping and multidimensional customer behaviors through partial memberships, in contrast to the rigid and oversimplify assignments of K-Means. These findings highlight the benefit of adopting FCM for e-commerce segmentation, as it provides more interpretable and actionable insights for personalized marketing. At the same time, the trade-off between clustering quality and computation time suggests that future research should explore optimization techniques such as parallelization, approximate fuzzy clustering, or hybrid models that combine the efficiency of hard clustering with the interpretability of soft clustering.
Analisis Pengaruh Pola Penggunaan Gadget Terhadap Computer Vision Syndrome Menggunakan Algoritma Machine Learning Ahmad, Hamna Izzatunnisa; Abdullah, Syahid; Chusyairi, Ahmad
Journal of Information and Technology Vol. 5 No. 1 (2025): Journal of Information and Technology Unimor (JITU)
Publisher : Department of Information Technology, Universitas Timor, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32938/jitu.v5i1.9138

Abstract

This research aims to analyze the impact of gadget usage on eye health using Decision Tree, Random Forest, and Naive Bayes algorithms. The increasing use of gadgets in society potentially causes eye health disorders, specifically Computer Vision Syndrome (CVS) symptoms that require in-depth investigation. Data was collected through a survey questionnaire about gadget usage habits and respondents' eye conditions. The OSEMN method was used to process and analyze data by applying three classification algorithms. Research findings showed the Random Forest algorithm provided the best performance with 73 % accuracy, followed by Naive Bayes at 65 %, and Decision Tree at 64 %. The study provides insights into the impact of gadget usage on eye health and recommendations for maintaining usage balance to prevent health disruptions.
Fuzzy C-Means Clustering Algorithm For Grouping Health Care Centers On Diarrhea Disease Chusyairi, Ahmad; Saputra, Pelsri Ramadar Noor; Zaenudin, Efendi
International Journal of Artificial Intelligence Research Vol 5, No 1 (2021): June 2021
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (290.71 KB) | DOI: 10.29099/ijair.v5i1.191

Abstract

In Indonesia, public health services at the city or district level are carried out by regional public hospitals or “puskesmas” (health care centers), especially in Banyuwangi regency, East Java, Indonesia that has 45 health care centers spread throughout the villages. This research focused on the deaths of babies caused by diarrhea diseases, which are the second leading cause of death among children younger than 5 years globally. All of the health care centers need to be divided into 3 groups to find out which health care centers have the least, most moderate, and many diarrhea sufferers. Fuzzy C-Means algorithm is used to overcome this problem. The result from this research shown that 2 health care centers have the smallest member of diarrhea sufferers, 14 health care centers have a medium member of diarrhea sufferers, and the rest have a large number of diarrhea sufferers. From the result of this study, it can be a reference for the health department center in dealing with diarrheal diseases, accordingly, the infant mortality rate due to diarrheal diseases can be lowered to health care centers that have high diarrhea sufferers.
KLASIFIKASI PRESTASI AKADEMIK PESERTA DIDIK DENGAN METODE MACHINE LEARNING DI SMP X Fandi Chriswantoro Putro; Ahmad Chusyairi; Cian Ramadhona Hassolthine
Jurnal Teknologi Informasi dan Komputer Vol. 11 No. 1 (2025): JUTIK : Jurnal Teknologi Informasi dan Komputer, Edisi April 2025
Publisher : LPPM Universitas Dhyana Pura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36002/jutik.v11i1.3751

Abstract

Machine learning (ML) is a field of science that focuses on designing and developing algorithmic models to create behavior based on available data. Academic achievement is a metric used for the assessment of quality educational institutions. By using academic data of students in SMP X and machine learning classification algorithms such as Random Forest, Naïve Bayes, k-Nearest Neighbors (k-NN), and Support Vector Machine (SVM), so that this research can classify the academic achievement of students in SMP X optimally seen from the comparison of the best accuracy rate among classification algorithms. The accuracy of an algorithm is a measure of how precisely it classifies a sample. Evaluation results are compared in the form of validation accuracy and standard deviation. The comparison is done to determine the best algorithm based on accuracy and stability. The results showed that the SVM algorithm has the highest validation accuracy with a value of 0.987410 which shows the best performance in predicting classes and the lowest standard deviation value of 0.005132 which shows a more stable and consistent performance, compared to other algorithms. This indicates that SVM excels in predicting the correct class with stable performance. Based on the results and analysis, it is concluded that the selected SVM algorithm is used to develop a classification model of students' academic achievement in the form of a python program that is still simple but has high accuracy, stable and consistent. This program has become a tool for SMP X in identifying students' academic achievement and as a material for reporting students' learning outcomes to parents.
Penerapan K-Nearest Neighbor Dengan Metode Euclidean Distance Untuk Klasifikasi Tingkat Ketebalan Cat Di PT XYZ Gunawan, Hendro; Chusyairi, Ahmad; Saputra, Muhamad Ikhwani
Jurnal Teknologi informasi dan Ilmu Komputer Vol. 1 No. 2 (2025): April 2025
Publisher : Nolsatu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65258/jutekom.v1.i2.12

Abstract

Pengecatan merupakan proses yang banyak dijumpai pada industri otomotif seperti perusahaan pembuatan pegas (leaf spring) untuk suku cadang (spare part) kendaraan. Pengklasifikasian tingkat pengecatan yang tepat akan bermanfaat dalam menentukan kualitas hasil produksi. Penelitian ini membahas tentang bagaimana proses pengecatan pada cat dasar dengan membandingkan tiga buah variabel yaitu ketebalan cat (thickness), tekanan udara (pressure), dan kekentalan cat (viscositas). Menggunakan perancangan model dan implementasi algoritma K-Nearest Neighbor dalam mengklasifikasikan suatu objek (data uji) berdasarkan kedekatan jarak dengan data latih. Algoritma K-NN merupakan salah satu metode supervised learning yang mengklasifikasikan data baru berdasarkan mayoritas kategori dari K tetangga terdekat. Algoritma ini digunakan karena kesederhanaannya dan kemampuan adaptasinya terhadap data yang tidak linear. Namun, tantangan utama dalam penggunaan K-NN adalah menentukan nilai K yang optimal dan biaya komputasi yang tinggi. Penelitian ini menggunakan metode Euclidean Distance untuk menghitung jarak antara data training dan data testing. Implementasi dilakukan pada dataset dengan label ‘Good' dan 'Not Good'. Hasil penelitian menunjukkan bahwa pemilihan nilai K yang optimal dan perhitungan jarak yang akurat menggunakan Euclidean Distance memungkinkan algoritma K-NN menghasilkan klasifikasi yang tepat dan dapat diandalkan. Penelitian ini juga menganalisis keunggulan dan keterbatasan K-NN serta faktor-faktor penting dalam penerapannya. Hasil penelitian menunjukkan bahwa algoritma K-NN berhasil diimplementasikan untuk klasifikasi ketebalan cat pada proses cat dasar di PT XYZ, dengan akurasi tertinggi sebesar 100% pada nilai K = 1. Dengan demikian, penelitian ini memberikan wawasan yang mendalam tentang aplikasi praktis K-NN dalam menyelesaikan permasalahan pada sebuah dataset yang menggunakan algoritma K-NN dengan metode Euclidean Distance.  
Co-Authors Abdu Aziz Muttaqin Abdurrahman, Raka Admiral Afifah Putri Witjaksono Ahmad, Hamna Izzatunnisa Aldian Fiqih Ardiyanto Alexander, Vincent Alvia Kharismasari Amrullah, Ridhwan Aslamiyah, Sulaibatul Azis, Afif Bagus Fikri Ananda Bintar Rudiawan Cian Ramadhona Hassolthine Dani Susilo Dela Harum Novia Sari Didik Setiyadi Diego Arif Mahardhikajaya Edi Santosa Eko Sasongko Estu Handayani Fachrurozi Alwi Fandi Chriswantoro Putro Fika Dwi Yuli Astutik Gunawan, Hendro Gusti Bagus Cahya Utama Hainah Wahyu Sofilia Haryanto, David Herocyma Ilfa Nurdina Ridho Iman Santoso Ismail Marzuki Ismail Marzuki Ismail Marzuki Jafar Shadiq M. Taufiq Marcell Dwi Purnomo Mayjen Tomy Sutanto Muhammad Hannan Muhammad Ikhwani Saputra Muktining Nur, Tri Kurnia Hadi Natasya Yasmin Kinanti Nur Jannah Nur Jannah Nur Jannah Pelsri Ramadar Noor Saputra Pradana, Ferry Pratama, Alexander Daniel Putra, Muhamad Iqbal Januadi Putri Nadia Ayu Rahmawati Rachmad Nur Hayat Rahmawati, Putri Nadia Ayu Riad Sahara Riskiyatun Hasanah Rizki Akbar Rully Pramudita Rully Pramudita Saludin Saludin Saludin Samhana Suryati Subari Samhana Suryati Subari Saputra, Muhamad Ikhwani Saputra, Pelsri Ramadar Noor Septira Kurniati Siregar, Jonathan Daniel Subari, Samhana Suryati Surya Hadi Kurniawan Syahid Abdullah To Bagus Setyo Adi Laksono Toto Haryanto Uus Rusmawan Yoyon Arie Budi Suprio Zaenudin, Efendi