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All Journal CommIT (Communication & Information Technology) JAIS (Journal of Applied Intelligent System) Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) EDUMATIC: Jurnal Pendidikan Informatika Jurnal Sistem Informasi dan Sains Teknologi Madaniya Pelita Teknologi : Jurnal Ilmiah Informatika, Arsitektur dan Lingkungan Innovative: Journal Of Social Science Research GLOBAL: Jurnal Lentera BITEP Jurnal Pelita Pengabdian Journal Innovation in Information and Computer Technology
Amali Amali
Universitas Pelita Bangsa
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Public Health Social Sciences Other

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FORECASTING WITH WEIGHTED MOVING AVERAGE METHOD FOR PRODUCT PROCUREMENT STOCK Amali Amali; Gatot Tri Pranoto; Muhammad Darwis
Jurnal Sistem Informasi dan Sains Teknologi Vol 4, No 2 (2022): Jurnal Sistem Informasi dan Sains Teknologi
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/sistek.v4i2.1268

Abstract

ABSTRACTDhanty Store is a family start-up located in East Jakarta. It was initiated in 2018, engaged in retail with the main product in the form of women's clothing and accessories. One of the important processes in Dhanty Store operations is the product procurement process. Currently, Dhanty Store request products according to their wishes without looking at their sales data. This causes their product stock is not well controlled. When there is a lot of demand, sometimes Dhanty Shops run out of stock so their customers will move to other stores. In addition, the process of requesting and procuring products to suppliers also takes a long time so that it can further disrupt the operations of Dhanty Store. This study develops a forecasting application prototype with the Weighted Moving Average method to assist Dhanty Store in the process of requesting and procuring their products. Forecasting results in the period (t) of the 1st week of January were 275 products. In addition, this study predicts product stock with a 4-week moving average and the MAD tracking signal value is ranged from -1.51 to 3.86 and the MAPE value is 35.4%. As for the reliability and level of user acceptance of the prototype model in this study, tested using the System Usability Scale (SUS) method and it is known that the average value given by respondents was 82 with details 0% considered inappropriate, 40% considered neutral and 60% rated it according to need.                                                                                                                                                        Keywords: data mining, forecasting, weighted moving average, MAD, MAPE, SUS
Manhattan, Euclidean And Chebyshev Methods In K-Means Algorithm For Village Status Grouping In Aceh Province Amali Amali; Gatot Tri Pranoto
Journal of Applied Intelligent System Vol 7, No 3 (2022): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v7i3.7037

Abstract

The Ministry of Villages, Development of Disadvantaged Regions and Transmigration (Ministry of Villages PDTT) is a ministry within the Government of Indonesia in charge of developing villages and rural areas, empowering rural communities, accelerating the development of disadvantaged areas, and transmigration. The 2014 Village Potential Data (Podes 2014) is data released by the Central Statistics Agency in collaboration with the Ministry of Villages PDTT in unsupervised form and consists of 6474 villages in the province of Aceh. Podes 2014 data is based on the level of village development (village specific) in Indonesia by using the village as the unit of analysis. Data mining is a method that can be used to group objects in a data into classes that have the same criteria (clustering). One of the algorithms that can be used for the clustering process is the k-means algorithm. Grouping data using k-means is done by calculating the shortest distance from a data point to a centroid point. In this study, a comparison of the distance calculation method on k-means between Manhattan, Euclidean and Chebyshev will be carried out. Tests will be performed using the execution time and the davies boulder index. From the tests that have been carried out, it is found that the number of villages in each cluster is 2,639 developing villages, 1,188 independent villages, 1,182 very underdeveloped villages, 1,266 developed villages and 199 disadvantaged clusters. The Chebyshev distance calculation method has the most efficient accumulation of time compared to Manhattan and Euclidean, while the Euclidean method has the most optimal Davies Index.
Perbandingan Algoritma Sentimen Analisis media data Twitter Pilgub Jabar 2018 Amali Amali
Jurnal Pelita Teknologi Vol 15 No 1 (2020): Maret 2020
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (572.205 KB) | DOI: 10.37366/pelitatekno.v15i1.298

Abstract

This paper is published to review and compare the performance of the SVM algorithms and CNN algorithms as an update composition in analyzing sentiment with tweeter attributes, the comparison of these algorithms using the Python application as a tool to support machine learning. The classification of negative, neutral and positive sentiments in the tweet dataset is tested and to determine and measure the accuracy, precision, recall, f_Measure and configuration matrix weights of both the SVM algorithms and CNN algorithms. The tools used with the Python Jupyter application, Tensorflow, Noted ++ are applied to the Indonesian language Twitter classification, the measurement results are precise and accurate according to human measurement parameters related to tweet data sentiment on Twitter social media commenting on the election of the Governor of West Java with the candidate for governor and deputy governor for the period 2018-2023. The results of this study, Testing Experiments before stemming with the SVM Algorithm was carried out seven times with an average accuracy rate of around 67%, and the CNN Algorithm before stemming also with seven trials with an average accuracy of around 67%, then the Testing Experiment after stemming. with SVM conducted seven trials the average accuracy rate was around 67%, while CNN algorithms before stemming was also carried out with seven trials with an average accuracy of about 52% lower than SVM algorithms
Perancangan Sistem Aplikasi Inventory Matrial Gudang Berbasis Web Dan Scan Barcode PT. Cabinindo Putra Angga Angga; Amali Amali; Agus Suwarno
Innovative: Journal Of Social Science Research Vol. 3 No. 3 (2023): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v3i3.3452

Abstract

Penelitian ini merumuskan masalah tentang bagaimana merancang dan mengimplementasikan sistem inventory material gudang di PT. Cabinindo Putra yang efisien dan efektif dibandingkan saat menggunakan pengelolaan manual dengan Ms. Excel. Sedangkan penelitian tujuan ini untuk pembuatan aplikasi sistem inventori berbasis website agar dapat mengoptimalkan proses pengadaan, penyimpanan, dan distribusi material. Penelitian ini menggunakan metode waterfall yaitu : analisis kebutuhan, perancangan sistem, implementasi, pengujian dan pemeliharaan. Pada tahap analisis kebutuhan, dilakukan survei dan wawancara dengan pihak terkait untuk memahami kebutuhan perusahaan dalam hal manajemen inventory material gudang. Selanjutnya dilakukan perancangan sistem yang mencakup desain database, antarmuka, dan logika bisnis yang mendukung pengelolaan inventori material gudang pengguna secara efisien. Implementasi sistem inventory material gudang dilakukan dengan membangun aplikasi berbasis website. Aplikasi ini memungkinkan pengguna untuk melacak dan mengelola inventory material gudang secara real-time, selanjutnya melakukan pengujian menggunakan metode black box dan white box dan melakukan pemeliharaan secara berkala. Hasil dari penelitian ini yaitu untuk mengoptimalkan pengelolaan inventaris gudang material, mengurangi operasional, dan meningkatkan kepuasan pelanggan melalui pengadaan material yang tepat waktu dan efisien.
Pemberdayaan Pelajar Digital Melalui Pengenalan Dasar Rekayasa Perangkat Lunak Amali Amali; Antika Zahrotul Kamalia; Wiyarno Wiyarno; Yefta Ondi Spalanzani Simatupang; Ahmad Amrullahalhafizh
Madaniya Vol. 6 No. 1 (2025)
Publisher : Pusat Studi Bahasa dan Publikasi Ilmiah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53696/27214834.1143

Abstract

Program Pemberdayaan Pelajar Digital melalui Pengenalan Dasar Rekayasa Perangkat Lunak (RPL) pada siswa SMPN 5 Cikarang Selatan bertujuan untuk meningkatkan keterampilan digital dan pemahaman dasar mengenai pengembangan perangkat lunak. Program ini mencakup pengenalan konsep dasar RPL, penggunaan alat pengembangan perangkat lunak, serta keterampilan pemrograman dan pengalaman berkoloborasi perancangan aplikasi RPL. Hasil evaluasi menunjukkan bahwa siswa berhasil mencapai hasil yang melebihi target pada hampir seluruh indikator yang ditetapkan, seperti pemahaman dasar RPL (90%), kemampuan menggunakan alat pengembangan perangkat lunak (85%), keterampilan pemrograman (80%), pemahaman etika digital (75%), dan kemampuan koloborasi (85%). Peningkatan ini mencerminkan efektivitas pendekatan yang menggabungkan teori dengan praktik langsung, serta kolaborasi. Program ini tidak hanya memberikan keterampilan teknis, tetapi juga membangun kesadaran siswa tentang etika dalam teknologi dan pentingnya kerjasama koloborasi. Secara keseluruhan, program ini berhasil mempersiapkan siswa untuk menghadapi tantangan di dunia digital dengan keterampilan yang relevan dan bertanggung jawab.
Advancing Cross-Cultural Natural Language Processing with a Focus on Sundanese Language and Contextual Nuances Anggi Muhammad Rifai; Ema Utami; Amali Amali; Muhamad Fatchan; Muhamad Ekhsan
CommIT (Communication and Information Technology) Journal Vol. 20 No. 1 (2026): CommIT Journal (in press)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The Sundanese language, as one of Indonesia’s regional tongues, holds deep cultural value but is still underrepresented in computational linguistics. The research addresses this gap by developing a translation model between Sundanese and Indonesian using a transformer-based sequence-to-sequence (Seq2Seq) architecture. With a parallel dataset of 3,616 sentence pairs, the model is fine-tuned to capture linguistic and contextual subtleties. The evaluation yields strong results: Bilingual Evaluation Understudy (BLEU) score of 44.12, Recall - Oriented Understudy for Gisting Evaluation (ROUGE)-1 F1-Score of 0.72, and ROUGE-L F1-Score of 0.71. Those demonstrate high translation quality despite limited data. Unlike earlier Sundanese translation studies that rely on Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), or standard transformer models, this research uniquely leverages the multilingual pretrained M2M100 Transformer, enabling transfer learning from high-resource languages to improve low-resource performance. These outcomes highlight the model’s potential for real-world applications, such as translation tools for education and cultural exchange. The research emphasizes the importance of improving access to Sundanese texts and promoting its digital presence to aid in language preservation. Overall, the research not only advances Natural Language Processing (NLP) research for low-resource languages but also reinforces the importance of integrating regional languages like Sundanese into modern technology. Building upon prior studies on Indonesian–Sundanese translation, the research novelty lies in fine-tuning a multilingual Seq2Seq Transformer that captures both linguistic and contextual nuances, thereby setting a new benchmark for lowresource language processing.
Implementation of Decision Tree C4.5 and Naïve Bayes for Data Mining-Based Chronic Kidney Disease Prediction Amali Amali; Candra Naya
Journal Innovation in Information and Computer Technology Vol. 2 No. 3 (2025): (September) Journal Innovation in Information and Computer Technology (JICTECH)
Publisher : PT. Altaf Publishing Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70895/jictech.v2i3.126

Abstract

Chronic Kidney Disease (CKD) is a serious health problem with increasing prevalence worldwide. Early detection is important to prevent severe kidney failure and reduce healthcare costs. This study aims to implement and compare the performance of Decision Tree C4.5 and Naïve Bayes algorithms for CKD prediction using a dataset from the UCI Machine Learning Repository containing 400 patient records and 24 clinical attributes. Data preprocessing included handling missing values, transforming categorical data into numerical form, and selecting relevant attributes. Model evaluation was conducted using 10-Fold Cross Validation with performance indicators including accuracy, precision, recall, and Area Under Curve (AUC). The results show that Decision Tree C4.5 achieved 93.00% accuracy, 84.27% precision, 100% recall, and 0.944 AUC. Meanwhile, Naïve Bayes obtained slightly better performance with 93.50% accuracy, 85.23% precision, 100% recall, and 0.948 AUC. The findings indicate that both algorithms are effective for CKD classification, although Naïve Bayes demonstrated better predictive performance, while Decision Tree C4.5 provided more interpretable classification rules. This research contributes to the development of intelligent decision support systems for early CKD diagnosis.
Prediction Analysis of Kartu Indonesia Pintar (KIP) Fund Recipients Using the Naïve Bayes Method Amali Amali; Nurhadi Surojudin
Journal Innovation in Information and Computer Technology Vol. 2 No. 2 (2025): (May) Journal Innovation in Information and Computer Technology (JICTECH)
Publisher : PT. Altaf Publishing Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70895/jictech.v2i2.127

Abstract

The Kartu Indonesia Pintar (KIP) program is a government initiative aimed at supporting students from economically disadvantaged families in obtaining proper educational opportunities. However, the distribution of KIP assistance is often considered inaccurate due to the complexity of determining eligible recipients based on multiple criteria. This study aims to analyze the implementation of the Naïve Bayes algorithm in predicting KIP recipients at Pelita Bangsa University. The research applies a data mining approach using several variables, including attendance, academic grades, parental dependents, housing conditions, and parents’ income. The dataset used in this study consists of 50 student data records that were processed through data selection and preprocessing stages before classification. The Naïve Bayes method was implemented and tested using the RapidMiner application to evaluate classification performance. The results show that the proposed model achieved an accuracy level of 96% with an error rate of 4%. In addition, evaluation using the Receiver Operating Characteristic (ROC) Curve produced an Area Under Curve (AUC) value of 0.979, indicating excellent classification capability. The findings demonstrate that the Naïve Bayes algorithm is effective and suitable for supporting objective decision-making in determining KIP assistance recipients based on predefined socioeconomic criteria.
Explainable DDoS Detection with a CNN-LSTM Hybrid Model and SHAP Interpretation Amali Amali; Anggi Muhammad Rifa'i; Edy Widodo; Ahmad Turmudi Zy; Dhani Ariatmanto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6865

Abstract

The rising frequency and complexity of Distributed Denial of Service (DDoS) attacks pose a severe threat to network security. This study aims to develop an effective and interpretable DDoS detection framework using a hybrid deep learning approach. The proposed method integrates Convolutional Neural Networks (CNN) to capture local traffic patterns and Long Short-Term Memory (LSTM) networks to model temporal dependencies. The CICIDS 2017 dataset, after preprocessing steps including data cleaning, standardization, and class balancing with SMOTE, was used to train and evaluate the model. Experimental results show that the framework achieved 99.98% accuracy and a 99.83% F1-Score, with minimal false positive and false negative rates. This study integrates SHAP to improve model interpretability, aligning feature importance with network security expertise. Future research will focus on real-time deployment, cross-dataset validation, and exploring alternative explainable AI techniques for improved scalability.
Demand-Based Product Classification Using K-Means with Intermittency Metrics Ariska Nur Anggraini; Amali Amali; Muhammad Syaibani Anwar
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.34435

Abstract

Inventory management at multi-SKU distribution companies becomes complex when most products have unstable and intermittent demand patterns. At PT JJA, procurement is still reactive without the use of historical patterns, while the previous approach generally relied on aggregate indicators such as average sales so that it has not been able to comprehensively capture temporal dynamics. This study aims to group products based on temporal demand patterns using K-Means Clustering in 11,988 transactions for the 2020–2025 period which are processed into 261 products through monthly aggregation, with features of average sales, coefficient of variation (CV), zero_month_ratio, Average Demand Interval (ADI), and trends. The results showed four optimal clusters (k = 4) with a Silhouette Score of 0.62 and an unbalanced distribution, where one cluster dominated 240 products. The values of zero_month_ratio (>0.80), ADI up to >12 months, and CV up to >3.5 show intermittent demand patterns and long-tail structures. The study confirms that the integration of temporal features (ADI, zero_month_ratio, CV, and trend) transforms the representation of demand from static aggregates to dynamic structures, while linking segmentation results with more adaptive procurement strategies to reduce the risk of overstock and understock.
Co-Authors Aditya Pratama Werdana Agung Nugroho Agus Suwarno Ahmad Amrullahalhafizh Ahmad Turmudi Zy Angga Angga Anggi Muhammad Rifa'i Anggi Muhammad Rifai Antika Zahrotul Kamalia Arif Susilo Ariska Nur Anggraini Candra Naya Dhani Ariatmanto Donny Maulana Edy Widodo Eko Budiarto Ema Utami Gatot Tri Pranoto M Syaibani Anwar Maha Putra Miftah Wangsadanureja Muhamad Ariel Saputra Muhamad Ekhsan Muhamad Fatchan Muhammad Darwis Muhammad Syaibani Anwar Nurhadi Surojudin Nurhidayanti, Nisa Wiyanto Wiyanto Wiyarno Yefta Ondi Spalanzani Simatupang Yusuf Putra Bintang Satria Zidan Lutfi Ramadhan