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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Informatika dan Teknik Elektro Terapan Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Teknoinfo J-SAKTI (Jurnal Sains Komputer dan Informatika) JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) JSI (Jurnal sistem Informasi) Universitas Suryadarma CSRID (Computer Science Research and Its Development Journal) Building of Informatics, Technology and Science Cyber Security dan Forensik Digital (CSFD) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Manajemen Informatika Jayakarta Journal of Students‘ Research in Computer Science (JSRCS) J-SAKTI (Jurnal Sains Komputer dan Informatika) J-Intech (Journal of Information and Technology) Jurnal Ilmiah Informatika dan Ilmu Komputer Bulletin of Informatics and Data Science VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Journal of Computer Science Contributions (Jucosco) The Indonesian Journal of Computer Science Journal of Informatics and Information Security Dinasti Information and Technology Journal of Information Technology
Alexander, Allan D
Informatika, Fakultas Ilmu Komputer, Universitas Bhayangkara Jakarta Raya
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Library & Information Science Materials Science & Nanotechnology

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Pengembangan Model Klasifikasi Citra Penyakit Daun Lada Menggunakan Jaringan Syaraf Tiruan Learning Vector Quantization (LVQ) Andrian Sah; Mulyadi Mulyadi; Allan Desi Alexander; Adam M Tanniewa
Jurnal Ilmiah Informatika dan Ilmu Komputer (JIMA-ILKOM) Vol. 4 No. 1 (2025): Volume 4 Nomor 1 March 2025
Publisher : PT. SNN MEDIA TECH PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jima-ilkom.v4i1.53

Abstract

Lada (Piper nigrum) adalah komoditas pertanian bernilai tinggi, namun rentan terhadap penyakit daun akibat infeksi jamur, bakteri, atau hama. Identifikasi dini penting untuk mencegah penurunan hasil panen, namun metode konvensional berbasis observasi visual sering subjektif dan membutuhkan keahlian khusus. Oleh karena itu, penelitian ini bertujuan untuk mengembangkan model klasifikasi penyakit daun lada menggunakan jaringan syaraf tiruan Learning Vector Quantization (LVQ) berbasis pengolahan citra digital. Proses penelitian dimulai dengan preprocessing, yang mencakup konversi ke ruang warna CIELAB untuk meningkatkan kontras, segmentasi menggunakan Otsu Thresholding, serta ekstraksi fitur warna dengan Mean Color dan fitur tekstur menggunakan Gray Level Co-occurrence Matrix (GLCM). Hasil ekstraksi fitur ini kemudian digunakan sebagai masukan untuk algoritma LVQ, yang melakukan klasifikasi berdasarkan pembelajaran vektor prototipe. Hasil evaluasi menunjukkan bahwa model LVQ yang dikembangkan mencapai tingkat akurasi keseluruhan sebesar 90,83%. Model menunjukkan performa terbaik dalam mengenali daun sehat dengan Precision, Recall, dan F1-Score sebesar 96,67%. Sementara itu, kelas Anthracnose memiliki Precision terendah sebesar 87,01%, dan kelas Leaf Blight menunjukkan Recall terendah sebesar 86,67% serta F1-Score terendah sebesar 88,14%. Meskipun terdapat variasi kinerja antar kelas, model ini terbukti efektif dalam menangani dataset terbatas, memiliki kemampuan klasifikasi yang baik terhadap data non-linear, serta memungkinkan interpretasi keputusan klasifikasi yang lebih jelas.
Sistem Pendukung Keputusan Pemilihan Wi-Fi Extender dengan Pendekatan Complex Proportional Assessment dan Rank Reciprocal Nurhasan Nugroho; Fryda Fatmayati; Allan Desi Alexander; Mursalim Tonggiroh
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.6984

Abstract

A Wi-Fi Extender is a device needed to expand the range and improve the quality of the Wi-Fi signal. To determine the choice, decision makers must know one by one the specifications of the existing products. This results in making decisions difficult and requiring a long time. So the aim of this research is to develop a decision support system for choosing a Wi-Fi Extender using the Rank Reciprocal and COPRAS (Complex Proportional Assessment) weighting approach to make it easier to make decisions in a relatively short time. The Rank Reciprocal approach is used to rank or weight the criteria given by decision makers. Meanwhile, the COPRAS approach is used to obtain the best alternative which is evaluated by calculating the effectiveness index directly proportional to the criteria considered to provide benefits and costs. Based on the case study that was carried out, the highest utility result was obtained, namely the Mercusys MW300RE (A4) which obtained a score of 100. The output produced by the decision support system in the case study that was carried out obtained the same score as manual calculations. Apart from that, the usability testing results obtained an average value of 88.75%. This shows that the system is declared suitable for use because it is in accordance with its function and use.
Implementasi Fuzzy Logic Pada Sistem Kontrol pH Air Mineral Berbasis IOT Joniwarta; Priatna, Wowon; Hamdani, Asep R.; Alexander, Allan D.
The Indonesian Journal of Computer Science Vol. 12 No. 4 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i4.3356

Abstract

Implementation of Fuzzy Logic in the IOT-Based Mineral Water pH Control System for various existing bottled mineral water products, this system can measure the pH value, to find out if the value is still within the limits suitable for consumption or not based on government regulations. The public finds it challenging to understand the level of the pH value of the product because the current state of information regarding the pH level of mineral water generally is not listed in the mineral water bottle circulating on the market. Hardware design, application design, and hardware and software integration were the three steps of this research project. The pH value will be read by this control system from the output of the sensors then the data is collected into a data set. The data will be examined for trends using fuzzy logic, which will be used to classify the maximum and minimum pH levels, acidity levels, and base levels. The study's findings show that an internet-based web of things can access the mineral water pH control system to ascertain each mineral water product's pH value and temperature. This information can then be used by consumers to ascertain the pH level of each mineral water product.
Determining Sales Patterns Using the Apriori Algorithm: A Case Study of Unlocked Cafe's Website Applications Rafika Sari; Nur Helmy; Allan Desi Alexander
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 12 No. 1 (2024): March 2024
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v12i1.8908

Abstract

In the business world, the sales process is the key to a company's success. Business processes will involve a lot of transaction data which will increase over time. This accumulation of data will not provide meaningful information if it is not processed and utilized properly. Correct decision making is obtained from accurate and informative data. This research was conducted to analyze, simulate and digitize data in the form of a website-based system which can be used as recommendations in making business decisions. The application of the Apriori algorithm supports system development in determining sales patterns by providing an overview of sales patterns for products of interest so that sales association rules are obtained. This research dataset was taken from transactions at the Unlocked Café & Coffee shop. The result of this research is an application designed according to the needs of business owners in the form of selecting itemsets by applying the Apriori algorithm to produce output in the form of product stock reference data and product sales patterns.
Evolution and Adaptation of Large Language Models for Bahasa Indonesia Allan Desi Alexander; Siti Setiawati
Dinasti Information and Technology Vol. 4 No. 1 (2026): Dinasti Information and Technology (July - September 2026)
Publisher : Dinasti Research & Yayasan Dharma Indonesia Tercinta (DINASTI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/dit.v4i1.3623

Abstract

This study evaluates the systematic evolution and computational adaptation of pre-trained language models and Large Language Models (LLMs) for Bahasa Indonesia and its low-resource regional dialects. Initially centered on bidirectional encoder-based representations like IndoBERT, the regional natural language processing (NLP) field has transitioned toward generative sequence-to-sequence structures and massive decoder-only architectures. This paper investigates the engineering methodologies of cross-lingual vocabulary adaptation, parameter initialization heuristics, and language-adaptive pre-training strategies designed to address text overfragmentation, representational misalignment, and tokenization cost inefficiencies. Through extensive structural benchmarks, this analysis compares discriminative and generative performances across tasks including sentiment classification, extractive question answering, text style normalization, domain-specific retrieval-augmented pipelines, and entity linking. While localized generative models such as Komodo, Sailor, and the SEA-LION suite improve contextual reasoning, colloquial style transfers, and regional dialect preservation, they remain susceptible to architectural anomalies like template leakage and entity hallucination. This study provides foundational benchmarks and methodological frameworks for adapting massive language models to morphologically rich, culturally diverse, and low-resource linguistic environments.
SISTEM MONITORING KEBAKARAN BERBASIS SMS GATEWAY Joniwarta Joniwarta; Allan D Alexander; Dwi Budi Srisulistiowati
JSI (Jurnal Sistem Informasi) Universitas Suryadarma Vol. 6 No. 2 (2019): JSI (Jurnal sistem Informasi) Universitas Suryadarma
Publisher : Fakultas Ilmu Komputer dan Desain - Unsurya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35968/jsi.v6i2.322

Abstract

Fire disasters that are not immediately dealt with can certainly cause losses both property losses and loss of life. This happens because some are good from short circuiting, throwing cigarette butts at random, exploding and burning objects and delaying information obtained by firefighters or school officials SMK Mandalahayu Bekasi during incidents of fire disasters. In this study a fire source fire Monitoring System Was Built Based On SMS Gateway At using MQ-135 as a smoke sensor which functions to detect smoke from fire, DS18B20 as a temperature sensor that functions to detect the temperature in the surrounding room, Buzzer alarm as a sound warning and SIM Module GSM900A as an SMS sending media, which is used to provide information about the detection of fire sources as early as possible, so that fire disasters are handled as soon as possible and the risk of fire can be minimized and routinely dominate the room using a camera Keywords: Fire,  Buzzer, SMS gateway
EVALUASI RISIKO ISPA PADA RUMAH TINGGAL BERBASIS PM2.5 DAN KELEMBABAN MENGGUNAKAN FUZZY LOGIC Joniwarta Joniwarta; Agus Hidayat; Allan Desi Alexander; Hendarman Lubis
Jurnal Manajamen Informatika Jayakarta Vol 6 No 2 (2026): JMI Jayakarta (April 2026)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jmijayakarta.v6i2.2377

Abstract

Kelembapan tinggi pada rumah tinggal, khususnya pada material gipsum, berpotensi meningkatkan pertumbuhan jamur dan akumulasi partikel halus (PM2.5) yang berkontribusi terhadap risiko Infeksi Saluran Pernapasan Akut (ISPA). Penelitian ini bertujuan untuk mengevaluasi risiko ISPA pada rumah tinggal dengan plafon/dinding gipsum lembap berdasarkan parameter konsentrasi PM2.5 dan kelembapan relatif (Relative Humidity/RH) menggunakan algoritma Fuzzy Logic. Evaluasi dilakukan dengan mengacu pada standar kualitas udara World Health Organization (WHO) tahun 2021 yang menetapkan batas ambang PM2.5 sebesar 15 µg/m³ untuk rata-rata 24 jam sebagai pedoman kesehatan. Sistem monitoring dikembangkan berbasis Internet of Things (IoT) menggunakan sensor PMS5003 untuk pengukuran PM2.5 dan DHT22 untuk kelembapan yang terintegrasi dengan mikrokontroler ESP32 serta dashboard pemantauan real-time. Hasil pengukuran selama 7 hari pada ruangan dengan gipsum lembap menunjukkan nilai RH rata-rata 73–82%, dengan konsentrasi PM2.5 berkisar antara 20–38 µg/m³, melampaui ambang WHO pada beberapa periode pengamatan. Analisis menggunakan metode Fuzzy Mamdani mengklasifikasikan kondisi tersebut ke dalam kategori “Waspada” hingga “Bahaya”, terutama ketika kelembapan tinggi memperkuat potensi pertumbuhan jamur dan bioaerosol. Hasil penelitian menunjukkan bahwa penggunaan algoritma Fuzzy Logic memberikan evaluasi risiko yang lebih adaptif dibandingkan metode threshold konvensional berbasis ambang WHO semata, karena mempertimbangkan interaksi antara PM2.5 dan kelembapan. Penelitian ini menegaskan pentingnya pengendalian kelembapan pada material gipsum dalam upaya pencegahan gangguan pernapasan di lingkungan rumah tinggal.
Model Prediksi Penyakit Jantung dengan Penanganan Outlier Menggunakan Interquartile Range dan Extreme Gradient Boosting Lukman Azhari; Novi Wulandari; Feru Adiningrat; Allan Desi Alexander
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6390

Abstract

Heart disease remains one of the leading causes of death worldwide, with increasing prevalence rates, including in Indonesia. Delayed detection and diagnosis are the main challenges in treating this disease, as most cases are only identified after patients experience serious symptoms or heart attacks. Medical data often containing outliers and noise adds to the complexity of developing accurate predictive models. This study aims to develop a heart disease prediction model using a combination of the Interquartile Range (IQR) method for outlier handling and the Extreme Gradient Boosting (XGBoost) algorithm for predictive modeling. The IQR method is applied at the pre-processing stage to identify and eliminate outliers robustly without reducing data integrity, while XGBoost is used to build an efficient prediction model through an ensemble learning approach. The results showed significant improvements in model performance, with accuracy increasing from 75.41% to 89.47% and AUC-ROC from 0.8615 to 0.9450. The model demonstrates balanced predictive capabilities with precision of 95.24% and recall of 80.00% for cases without disease, and precision of 86.11% and recall of 96.88% for cases with disease. The developed model makes significant contributions by improving data quality through robust outlier handling using the IQR method, building a more accurate prediction model by leveraging the advantages of the XGBoost algorithm in the ensemble learning approach.
Studi Perbandingan Model Keamanan Data pada Cloud Computing Allan Desi Alexander
Journal of Informatic and Information Security Vol. 6 No. 2 (2025): Desember 2025
Publisher : Program Studi Informatika, Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/01vjnj44

Abstract

Layanan komputasi awan (cloud computing) telah menjadi tulang punggung transformasi digital global, menawarkan skalabilitas, efisiensi biaya, dan fleksibilitas yang belum pernah ada sebelumnya. Namun, perpindahan data dari infrastruktur lokal ke lingkungan pihak ketiga yang bersifat multi-tenant menimbulkan kekhawatiran serius terhadap keamanan dan privasi data. Laporan penelitian ini menyajikan analisis komprehensif mengenai perbandingan model keamanan data dalam ekosistem cloud, mencakup aspek kriptografi, mekanisme kontrol akses, dan strategi manajemen risiko. Melalui tinjauan literatur sistematis terhadap studi yang terindeks Scopus dan SINTA antara tahun 2013 hingga 2025, penelitian ini mengevaluasi kinerja algoritma enkripsi seperti AES, RSA, dan ECC, serta membandingkan efektivitas model Role-Based Access Control (RBAC) dan Attribute-Based Access Control (ABAC). Temuan utama menunjukkan bahwa algoritma simetris seperti AES unggul dalam kecepatan dan efisiensi memori untuk data massal, sementara model asimetris seperti RSA lebih optimal untuk manajemen kunci. Dalam hal kontrol akses, ABAC menawarkan fleksibilitas yang lebih tinggi untuk lingkungan dinamis dibandingkan RBAC yang bersifat statis, meskipun memiliki kompleksitas implementasi yang lebih besar. Penelitian ini juga menyoroti peran teknologi mutakhir seperti blockchain, machine learning, dan federated learning dalam memperkuat postur keamanan cloud serta memberikan kerangka kerja manajemen risiko bagi organisasi yang melakukan migrasi data..  
Studi Klasifikasi Presisi Kesesuaian Lahan Pertanian Menggunakan Algoritma Extreme Gradient Boosting (XGBoost) Benardus Gunawan Sudarsono; Allan Desi Alexander
Journal of Informatic and Information Security Vol. 7 No. 1 (2026): Juni 2026
Publisher : Program Studi Informatika, Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/b7c8py98

Abstract

The application of artificial intelligence technology in the agricultural sector is the main foundation in the paradigm shift towards sustainable precision agriculture. This study presents a comprehensive analysis of the application of the Extreme Gradient Boosting (XGBoost) algorithm to predict the suitability of crop types based on soil chemical characteristics and macro-environmental conditions. Model evaluation was conducted using the benchmark dataset Crop Recommendation Dataset accessed through the Kaggle platform. This dataset has a perfect class balance with a total of 2,200 samples evenly divided into 22 agricultural commodities. The developed predictive model evaluates seven soil and climate biophysical parameters, namely nitrogen, phosphorus, potassium, air temperature, relative humidity, soil acidity (pH), and rainfall intensity. The test results show that the XGBoost algorithm ranks top in classification accuracy with values ​​ranging from 99.31% to 99.77%, surpassing other ensemble algorithms such as Random Forest as well as traditional models such as Decision Tree and Naive Bayes. Feature contribution analysis demonstrates that climate parameters (rainfall and humidity) act as primary ecological filters at the macro-level, while soil macronutrient (NPK) ratios serve as secondary determinants at the crop-specific micro-level. Overall, this boosting-based ensemble approach offers high accuracy and robustness to data outliers, making it a highly reliable agronomic decision-making tool for supporting sustainable land productivity.
Co-Authors Adam M Tanniewa Agung Dwi Prasojo Agus Hidayat Ajie Prasetya Alfry Aristo Jansen Sinlae Andrian Sah Andy Achmad Hendharsetiawan Annisa Oktavianti Hermadi Asep R. Hamdani Azhari, Lukman Benardus Gunawan Sudarsono Chusna, Nuke L. Dani Yusuf Daniarti, Yeni Destriana, Rachmat Dwi Budi Srisulistiowati Fajar Septian Fauzan Kamil Feru Adiningrat Firdaus, Imansyah Abinda Fryda Fatmayati Fryda Fatmayati Hamdani, Asep R. Hantoro, Kusdarnowo Hendarman Lubis Hendarman Lubis Hendarman Lubis Joniwarta Joniwarta Kamil, Fauzan Lestari, Tyastuti Sri Lubis, Hendarman Lukman Azhari Lukman Azhari Muhammad Khaerudin Mulyadi Rusli Mursalim Tonggiroh Nofitri Heriyani Novi Wulandari Nur Helmy Nurdiana Handayani Nurfiyah Nurhasan Nugroho nurnaningsih, Desi Pamnungkas, R Wisnu Prio Prasetya, Ajie Prasojo, Agung Dwi Priatna , Wowon RAFIKA SARI Ratna Salkiawati Reza, Ali Rini Nuraini Riyanto, Umbar Shalahudin, Mohammad Imam Siti Setiawati Soares, Teotino Gomes Tonggiroh, Mursalim Yanuardi Yanuardi Yanuardi Yanuardi