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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 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) Informatika Sains Teknologi Sistematis JUTEKOM - Jurnal Teknologi dan Ilmu Komputer
Chusyairi, Ahmad
Universitas Siber Asia
Aerospace Engineering Arts Humanities Chemical Engineering, Chemistry & Bioengineering 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.
Implementasi Sistem Kalkulasi Berat Dan Iuran Sampah Berbasis Internet Of Things Pada Perumahan Bekasi Jaya Indah Abdu Aziz Muttaqin; Ahmad Chusyairi
INFORMATIKA SAINS TEKNOLOGI Vol 2 No 2 (2024): Jurnal insit Vol 2 No 2 Tahun 2024
Publisher : Fakultas Sains Dan Teknologi Universitas Islam Asyafiiyah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34005/insit.v2i2.4070

Abstract

Waste is a complex problem in developing countries like Indonesia, negatively impacting the environment and public health. Bekasi Jaya Indah Housing Estate, Bekasi City, faces the challenge of sub-optimal waste management. One of them is the gap between waste fees and the weight of waste generated, especially from houses with home industry activities. This research aims to improve monitoring efficiency by developing a tool based on firebase technology or user interface. This technology allows real-time access from various locations to monitor environmental conditions. This tool is not only a monitor of environmental conditions, but also a management tool to support better decision making in maintaining environmental stability and safety. The method used is the prototype method with the stages of communication, quick plan, quick design modeling, construction of prototype, and deployment delivery & feedback. This monitoring tool, it is expected that residents, officers, and administrators can easily monitor environmental conditions from various locations. User have the ability to take the necessary response actions quickly based on real-time information obtained from this monitoring tool.
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).
Prediksi Penjualan Untuk Optimasi Stock Produk Menggunakan Algoritma Long Short Term Memory Susilo, Dani; Ahmad Chusyairi; Saputra, Muhammad Ikhwani
Sistematis Vol. 1 No. 2 (2025): April 2025
Publisher : CV.RIZANIA MEDIA PRATAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69533/56gyat30

Abstract

Perusahaan distribusi menjadi pihak yang bertanggung jawab atas proses penyaluran barang dan menjadi perantara antara produsen dengan konsumen. Permasalahan utama yang sering dihadapi perusahaan distribusi adalah terkait dalam pengadaan stok barang yang dapat menyebabkan kelebihan atau kekurangan  stok. Penelitian terkait distribusi barang lebih fokus pada pendekatan sederhana atau pemodelan berbasis metode klasik, yang kurang efektif dalam meramalkan penjualan dengan tingkat akurasi yang tinggi. Oleh karena itu, pengembangan model prediksi berbasis algoritma deep learning, seperti Long Short-Term Memory (LSTM), yang dapat menangani dependensi jangka panjang dalam data time series, masih terbatas dalam konteks perusahaan distribusi, khususnya dalam meningkatkan efisiensi pengelolaan stok barang dan pengurangan kesalahan pengadaan stok.Tujuan penelitian ini untuk mengembangkan model prediksi penjualan menggunakan algoritma Long Short Term Memory (LSTM) guna meningkatkan efisiensi pengelolaan stok barang pada perusahaan distribusi XYZ. Dengan memanfaatkan data historis penjualan yang berbentuk time series, penelitian ini memprediksi penjualan di masa depan dan menghasilkan prediksi per produk dan per bulan. Evaluasi model menggunakan Mean Absolute Percentage Error (MAPE) menghasilkan tingkat kesalahan rata-rata sebesar 3,60%, hal ini menunjukkan bahwa model memiliki akurasi yang sangat akurat. Hasil prediksi ini diintegrasikan kedalam sistem pengadaan stok untuk mengoptimalkan rekomendasi pengadaan stok dalam proses pembelian barang. Penelitian ini menunjukkan bahwa penerapan  LSTM dalam prediksi penjualan dapat menjadi solusi efektif bagi perusahaan distribusi dalam pengelolaan stok dan efisiensi biaya operasional.
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.
Identification of Stock Breakouts Using Support Vector Machine with Integrated Fundamental Data and Random Forest Prediction Utama, Gusti Bagus Cahya; Chusyairi, Ahmad; Sahara, Riad
Faktor Exacta Vol 18, No 1 (2025)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v18i1.27805

Abstract

Assessment decisions of independent learning activities using SMART-FCM method Saputra, Pelsri Ramadar Noor; Aslamiyah, Sulaibatul; Chusyairi, Ahmad
JURNAL INFOTEL Vol 15 No 1 (2023): February 2023
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v15i1.888

Abstract

Sekolah Tinggi Ilmu Komputer (STIKOM) PGRI Banyuwangi implemented the Independent Learning - Independent Campus (MBKM) activity for two semesters. The results of student assessments for MBKM activities for one semester are influenced by the results of daily and weekly logbook monitoring, monitoring and evaluation assessments, and assessments of supervisors, examiners, and work partners. Assessments that are less objective cause many students to get good grades even though the implementation of MBKM activities is not well. The Simple Multi-Attribute Rating Technique (SMART) method is used to produce student eligibility group data and a more objective assessment. The results of the SMART calculations are combined with the Fuzzy C-Means (FCM) algorithm so that the results of grouping student data are more appropriate based on the similarities and characteristics of the members. Silhouette Coefficient is used to compare the grouping results. The results obtained that the use of SMART-FCM is better than the SMART results because it has a Silhouette Coefficient value close to 1 of 0.31187.
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.
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 Dela Harum Novia Sari Didik Setiyadi Diego Arif Mahardhikajaya Dwi Purnomo, Marcell Edi Santosa Eko Sasongko Estu Handayani Fachrurozi Alwi Fandi Chriswantoro Putro Fika Dwi Yuli Astutik Gunawan, Hendro Hainah Wahyu Sofilia Haryanto, David Herocyma Ilfa Nurdina Ridho Iman Santoso Ismail Marzuki Ismail Marzuki Ismail Marzuki Jafar Shadiq M. Taufiq Mayjen Tomy Sutanto 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, Muhammad Ikhwani Saputra, Pelsri Ramadar Noor Septira Kurniati Siregar, Jonathan Daniel Subari, Samhana Suryati Sulaibatul Aslamiyah Surya Hadi Kurniawan Susilo, Dani Syahid Abdullah To Bagus Setyo Adi Laksono Toto Haryanto Utama, Gusti Bagus Cahya Uus Rusmawan Yoyon Arie Budi Suprio Zaenudin, Efendi