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All Journal JURTEKSI JOURNAL OF SCIENCE AND SOCIAL RESEARCH Community Development Journal: Jurnal Pengabdian Masyarakat J-Com (Journal of Computer) International Journal of Management Science and Information Technology (IJMSIT)
Nurwati Nurwati
Universitas Royal
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Health Professions Public Health Social Sciences

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THE BEST LAPTOP RATING DECISION SUPPORT SYSTEM FOR MOORA BASED CUSTOMERS IN THE TECH KIOS LAPTOP KISARAN Fitri Yasmin Khairani; Nurwati Nurwati; Santoso Santoso
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 12 No. 3 (2026): Juni 2026
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v12i3.4434

Abstract

Abstract: Tech Kios Laptop Kisaran is a business engaged in selling used laptops with various brands and specifications to meet customer needs. However, the selection process is still conducted manually and relies on subjective judgment, which may result in less accurate recommendations. This study aims to design and implement a Decision Support System using the MOORA (Multi-Objective Optimization on the Basis of Ratio Analysis) method to objectively determine the best used laptop. The criteria applied in this study include brand, screen resolution, laptop size, and battery durability. The system was developed through requirement analysis, system design, implementation, and black-box testing. The results show that the system successfully generates rankings based on MOORA preference values. The highest optimization value of 0.4321 was achieved by Lenovo IdeaPad Slim (A04) and Lenovo ThinkPad (A06), indicating that these two alternatives are the best recommended used laptops. Therefore, the developed system enhances the objectivity, effectiveness, and accuracy of the laptop selection process at Tech Kios Laptop Kisaran. Keywords: decision support system; MOORA; multi criteria; used laptop; recommendation. Abstrak: Tech Kios Laptop Kisaran merupakan usaha yang bergerak di bidang penjualan laptop bekas dengan berbagai merek dan spesifikasi untuk memenuhi kebutuhan pelanggan. Namun, proses pemilihan laptop masih dilakukan secara manual dan bergantung pada penilaian subjektif, sehingga berpotensi menghasilkan rekomendasi yang kurang akurat. Penelitian ini bertujuan untuk merancang dan mengimplementasikan Sistem Pendukung Keputusan menggunakan metode MOORA (Multi-Objective Optimization on the Basis of Ratio Analysis) guna menentukan laptop bekas terbaik secara objektif. Kriteria yang digunakan dalam penelitian ini meliputi merek, resolusi layar, ukuran laptop, dan ketahanan daya baterai. Pengembangan sistem dilakukan melalui tahapan analisis kebutuhan, perancangan sistem, implementasi, serta pengujian menggunakan metode black-box. Hasil penelitian menunjukkan bahwa sistem mampu menghasilkan peringkat alternatif berdasarkan nilai preferensi MOORA. Nilai optimasi tertinggi sebesar 0,4321 diperoleh oleh Lenovo IdeaPad Slim (A04) dan Lenovo ThinkPad (A06), yang menunjukkan bahwa kedua alternatif tersebut merupakan rekomendasi laptop bekas terbaik. Dengan demikian, sistem yang dikembangkan mampu meningkatkan objektivitas, efektivitas, dan ketepatan dalam proses pemilihan laptop bekas di Tech Kios Laptop Kisaran. Kata kunci: laptop bekas; MOORA; multi-kriteria; rekomendasi; sistem pendukung keputusan.
Penggunaan Metode Moora Untuk Mengukur Kinerja Sales Di Pt Panca Niaga Jaya Lestari Amila Purnama Sari Simbolon; Nurwati Nurwati; Mustika Fitri Larasati
J-Com (Journal of Computer) Vol. 6 No. 1 (2026): MARET 2026
Publisher : STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/j-com.v6i1.4041

Abstract

Abstract: The advancement of information technology has significantly impacted the business world, including the measurement of sales performance. PT Panca Niaga Jaya Lestari, as a food distribution company, still conducts performance evaluations manually, which leads to subjectivity and delays in the evaluation process. This study aims to design a web-based decision support system using the MOORA method to produce objective, fast, and accurate assessments. The system is developed using PHP and MySQL, with evaluation criteria including discipline, teamwork, sales volume, and length of service. The implementation results show that the system is capable of performing calculations and ranking sales performance in a measurable manner. This application assists supervisors and managers in conducting evaluations and accelerates decision-making processes. The final results indicate that Afril Handoko achieved the highest score of 0.3802. This system is effective in improving efficiency and reducing subjectivity in sales performance evaluation. Keywords: decision support system; sales performance; MOORA; PHP; MySQL Abstrak: Perkembangan teknologi informasi memberikan dampak besar pada dunia bisnis, termasuk dalam pengukuran kinerja sales. PT Panca Niaga Jaya Lestari sebagai perusahaan distributor pangan masih melakukan penilaian secara manual, sehingga menimbulkan subjektivitas dan keterlambatan evaluasi. Penelitian ini bertujuan merancang sistem pendukung keputusan berbasis web menggunakan metode MOORA untuk menghasilkan penilaian yang objektif, cepat, dan akurat. Sistem dikembangkan dengan PHP dan MySQL, dengan kriteria penilaian meliputi kedisiplinan, kerja sama tim, jumlah penjualan, dan masa kerja. Hasil implementasi menunjukkan sistem mampu melakukan perhitungan dan perangkingan kinerja sales secara terukur. Aplikasi ini memudahkan supervisor dan manajer dalam evaluasi serta mempercepat pengambilan keputusan. Hasil akhir menunjukkan Afril Handoko memperoleh nilai tertinggi sebesar 0,3802. Sistem ini efektif meningkatkan efisiensi dan mengurangi subjektivitas dalam penilaian kinerja sales. Kata kunci: sistem pendukung keputusan; kinerja sales; MOORA; PHP; MySQL
Implementation of SCM System in Distribution and Stock at Ozan Glass Store Mufti Hafiz; Nurwati Nurwati; Abdul Karim Syahputra
International Journal of Management Science and Information Technology Vol. 6 No. 1 (2026): January - June 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijmsit.v6i1.7351

Abstract

The increasingly rapid development of the business world requires business actors to be able to manage their operational activities more effectively and efficiently. In this study, problems that often occur at the Ozan Glass Shop, such as the process of managing product and inventory data, which is still carried out simply, so that there is often a discrepancy between stock data and actual conditions. The unintegrated inventory monitoring process also makes it difficult to control stock levels and has the potential to cause shortages or stockpiles of goods. These conditions cause the available information to be less than optimal as a basis for decision making, so that the implementation of a Supply Chain Management (SCM) system is needed to improve the effectiveness of inventory management and product distribution. In addition, through the main discussion carried out in this study aims to be able to understand the process of product distribution from suppliers to the Ozan Glass Shop and its obstacles, determine the implemented stock management and monitoring system and analyze distribution and inventory to improve operational efficiency and reduce problems that occur in the store's business activities. Based on the overall description in this study, the author can conclude that the implementation of the Supply Chain Management (SCM) system at the Ozan Glass Shop has succeeded in improving the effectiveness of inventory management and product distribution. Before the system was implemented, operational processes were still carried out manually, resulting in frequent stock data discrepancies, delays in information, recording errors and difficulties in monitoring order status and distribution. This research resulted in a web-based SCM system capable of integrating stock management, orders, procurement, and distribution within a single platform. Implementation results show that the system can provide real-time inventory information, improve data accuracy, accelerate administrative processes, and facilitate product distribution monitoring. Furthermore, the system is capable of generating more structured and informative reports, supporting faster and more accurate decision-making. The system's implementation also resulted in increased operational efficiency, reduced recording errors, and improved customer service quality.
OPTIMALISASI MEDIA SOSIAL DESA BERBASIS SISTEM INFORMASI SEBAGAI SARANA INFORMASI PUBLIK DAN PROMOSI POTENSI LOKAL Nurwati Nurwati; William Ramdhan; Junaidi Sholat
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 7 No. 3 (2026): Vol. 7 No. 3 (2026)
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v7i3.61173

Abstract

Perkembangan teknologi informasi telah mendorong transformasi digital dalam penyelenggaraan pemerintahan desa, khususnya dalam penyampaian informasi publik dan promosi potensi lokal. Namun, pemanfaatan media sosial di Desa Prapat Janji, Kabupaten Asahan, masih belum optimal karena rendahnya literasi digital aparatur desa, belum adanya pengelolaan konten yang terstruktur, serta belum terintegrasinya media sosial dengan sistem informasi desa. Kegiatan Pengabdian kepada Masyarakat (PKM) ini bertujuan untuk mengoptimalkan pemanfaatan media sosial berbasis sistem informasi sebagai sarana informasi publik dan promosi potensi lokal desa. Metode yang digunakan meliputi observasi lapangan, identifikasi kebutuhan mitra, pelatihan literasi digital, pendampingan pengelolaan media sosial, implementasi sistem informasi desa berbasis web, serta monitoring dan evaluasi. Hasil kegiatan menunjukkan adanya peningkatan pengetahuan dan keterampilan aparatur desa dalam mengelola media sosial secara profesional, menyusun konten digital yang informatif, serta memanfaatkan website desa sebagai media pelayanan informasi publik. Selain itu, media sosial desa mulai dimanfaatkan sebagai sarana promosi hasil pertanian, produk UMKM, dan potensi sosial budaya sehingga mampu memperluas jangkauan informasi kepada masyarakat. Program ini memberikan dampak positif terhadap peningkatan kualitas pelayanan informasi publik, transparansi pemerintahan desa, serta penguatan branding desa melalui pemanfaatan teknologi digital. Keberlanjutan program diharapkan dapat diwujudkan melalui komitmen pemerintah desa dalam melakukan pembaruan informasi secara berkala dan pengembangan sistem informasi sesuai kebutuhan masyarakat.
ANALISIS SELEKSI FITUR PADA NETWORK INTRUSION DETECTION DENGAN NATURAL LANGUAGE PROCESSING Riki Andri Yusda; Sahren Sahren; Nurwati Nurwati; Shaqilla Swita Sarah; Dava Erlangga
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 4 (2026): August 2026 (1)
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i4.6883

Abstract

This study proposes a Natural Language Processing (NLP)-based feature selection approach for Network Intrusion Detection Systems (NIDS) using the CICIDS2017 dataset. To address high computational complexity caused by high dimensionality, network flows are normalized using Min-Max Scaling and transformed into textual representations via a consistent sentence template. The text data is then weighted using Term Frequency–Inverse Document Frequency (TF-IDF), followed by Chi-Square (Chi²) feature selection to isolate the most discriminative features. This process yields a dataset comprising 691,406 text documents with 302 textual features. Performance is evaluated using Random Forest, Linear Support Vector Machine (Linear SVM), and Naïve Bayes classifiers. Experimental results show that Random Forest achieves the highest accuracy at 97.3%, followed by Linear SVM at 96.4%, and Naïve Bayes at 83.2%. The confusion matrix further confirms that the vast majority of network traffic instances are correctly classified. These findings demonstrate that transforming tabular network data into textual formats using NLP, combined with TF-IDF and Chi-Square feature selection, successfully produces representative features, enhances intrusion detection performance, and effectively reduces data complexity in NIDS.
PENERAPAN ALGORITMA K-PROTOTYPES DALAM ANALISIS POTENSI UMKM DI KABUPATEN ASAHAN William Ramdhan; Nurwati Nurwati; Santoso Santoso
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 8 No. 3 (2025): August 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i3.3863

Abstract

Abstract: Micro, Small, and Medium Enterprises (MSMEs) in Asahan Regency have very heterogeneous characteristics, both in terms of business sectors, capital size, and income achievement. This study aims to cluster MSMEs using the K-Prototypes algorithm to group business actors into uniform clusters based on numerical and categorical characteristics. The methodology used includes the pre-processing stage, variable transformation, determining the optimal number of clusters using the elbow method, and implementing the K-Prototypes algorithm. The results of the study showed that five main clusters were successfully formed, each showing a different pattern in terms of capital, net income, and dominant business sector. Data visualization and exploration (EDA) also strengthened the understanding of the cluster structure that was formed. The cluster with the highest capital and income is dominated by the medium-scale trade sector, while the cluster with the lowest capital and income is identical to micro MSMEs in the culinary and service sectors. These findings prove that the K-Prototypes algorithm is effective in identifying MSME segmentation in a more structured manner and can be the basis for designing more targeted MSME development strategies. Keyword: UMKM; clustering; K-Prototypes; mixed data; segmentation analysis. Abstrak: Usaha Mikro, Kecil, dan Menengah (UMKM) di Kabupaten Asahan memiliki karakteristik yang sangat heterogen, baik dari sisi sektor usaha, besaran modal, hingga capaian income. Penelitian ini bertujuan untuk melakukan klasterisasi UMKM menggunakan algoritma K-Prototypes guna mengelompokkan pelaku usaha ke dalam klaster-klaster yang seragam berdasarkan karakteristik numerik dan kategorikal. Metodologi yang digunakan mencakup tahap pre-processing, transformasi variabel, penentuan jumlah klaster optimal menggunakan metode elbow, serta implementasi algoritma K-Prototypes. Hasil penelitian menunjukkan bahwa lima klaster utama berhasil dibentuk, masing-masing menunjukkan pola yang berbeda dalam hal modal, income bersih, dan sektor usaha dominan. Visualisasi dan eksplorasi data (EDA) turut memperkuat pemahaman terhadap struktur klaster yang terbentuk. Klaster dengan modal dan income tertinggi didominasi oleh sektor perdagangan skala menengah, sedangkan klaster dengan modal dan income terendah identik dengan UMKM mikro di sektor kuliner dan jasa. Temuan ini membuktikan bahwa algoritma K-Prototypes efektif digunakan untuk mengidentifikasi segmentasi UMKM secara lebih terstruktur dan dapat menjadi dasar dalam merancang strategi pengembangan UMKM yang lebih tepat sasaran. Kata kunci: UMKM; klasterisasi; K-Prototypes; data campuran; analisis segmentasi
ANALISIS DATA EKSPLORATORI DAN CLUSTERING K-MODES UNTUK PEMETAAN STATUS GIZI BALITA PADA KASUS STUNTING DI KABUPATEN ASAHAN William Ramdhan; Nurwati Nurwati; Elly Rahayu
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 8 No. 4 (2025): November 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i4.4192

Abstract

Stunting is a chronic nutritional problem that impacts children's physical growth and development and is a public health challenge in Indonesia. This study integrates Exploratory Data Analysis (EDA) and K-Modes Clustering to map the nutritional status of toddlers in stunting cases in Asahan Regency. EDA was used to explore data distribution, identify relationships between nutritional status indicators, and identify risk factor patterns, while K-Modes was used to group toddlers based on shared categorical characteristics. The dataset used included sociodemographic variables (gender, age category, parental education and occupation) and nutritional status indicators (weight/age, height/age, weight/height). The analysis results showed a moderate positive correlation between weight/age and height/age (0.32) and a strong negative correlation between weight/age and weight/height (-0.50), indicating a link between stunting and wasting. The application of K-Modes resulted in three main clusters: Cluster 0, dominated by female toddlers with normal nutritional status but low parental education; Cluster 1 consists of infant girls with low weight for age and short height for age, despite most parents having a high school education; Cluster 2 contains infant boys with very low weight for age and very short height for age, and relatively low maternal education. The profile of each cluster was analyzed to identify dominant characteristics relevant for intervention. This integrative approach demonstrates that the combination of EDA and K-Modes is able to provide a comprehensive picture of variations in toddler nutritional status, thus serving as a basis for planning more targeted promotive and stunting prevention strategies at the regional level.
ANALISIS MODEL KLASIFIKASI DENGAN OPTIMASI PARTICLE SWARM OPTIMIZATION DALAM KLASIFIKASI STATUS GIZI ANAK Nurwati Nurwati; Rika Nofitri; Diana Selvi
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 8 No. 4 (2025): November 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i4.4247

Abstract

Abstract: Nutritional status represents a state of equilibrium in the form of specific variables or the manifestation of nutrition in the form of specific variables. Optimal nutritional status is achieved when there is a balance between nutrient intake and nutrient requirements. Malnutrition not only affects an individual's physical health but also negatively impacts their overall development and well-being. However, data warehouses related to child nutritional status in Asahan Regency from various community health centers (Puskesmas) have not been optimally utilized to generate valuable information. Therefore, the purpose of this study was to analyze child nutrition datasets using a machine learning-based classification model that can predict children's nutritional status early. The classification model was chosen because of its ability to group data or objects based on specific classes, making it suitable for future data prediction. Based on the results, it can be concluded that optimizing the toddler nutritional status classification model using the Particle Swarm Optimization (PSO) method and cross-validation was able to identify the best model, namely the Decision Tree, with an accuracy of 37.50%. Although the overall accuracy of all models was still relatively low. This indicates the need for further data processing, such as data balancing, selecting more relevant features, and further parameter tuning to improve classification performance. Keywords: Classification; Particle_Swarm Optimization; Cross Validation, Nutritional Status Abstrak: Status gizi adalah representasi dari keadaan keseimbangan dalam bentuk variabel tertentu atau manifestasi nutrisi dalam bentuk variabel tertentu, di mana status gizi optimal dicapai ketika terjadi keseimbangan antara asupan dan kebutuhan zat gizi. Gizi buruk tidak hanya memengaruhi kesehatan fisik individu, tetapi juga berdampak negatif pada perkembangan dan kesejahteraan mereka secara keseluruhan. Akan tetapi, gudang data yang terkait status gizi anak di Kabupaten Asahan dari berbagai puskesmas belum dimanfaatkan dengan optimal untuk menghasilkan informasi yang berharga. Oleh karena itu, tujuan dari penelitian ini adalah untuk menganalisis dataset gizi anak menggunakan model klasifikasi berbasis machine learning yang dapat memprediksi status gizi anak secara dini. Model klasifikasi dipilih karena kemampuannya dalam mengelompokkan data atau objek berdasarkan kelas tertentu, sehingga cocok untuk prediksi data di masa depan. Berdasarkan hasil penelitian, dapat disimpulkan bahwa optimasi model klasifikasi status gizi balita menggunakan metode Particle Swarm Optimization (PSO) dan cross validation mampu mengidentifikasi model terbaik, yaitu Decision Tree dengan akurasi 37,50%, meskipun secara umum tingkat akurasi seluruh model masih relatif rendah. Hal ini menunjukkan perlunya pengolahan data lebih lanjut, seperti balancing data, pemilihan fitur yang lebih relevan, maupun tuning parameter lanjutan untuk meningkatkan performa klasifikasi. Kata kunci: Klasifikasi; Particle Swarm Optimization; Cross Validation, Status Gizi 
Co-Authors Abdul Karim Syahputra Amila Purnama Sari Simbolon Dava Erlangga Diana Selvi Elly Rahayu Fitri Yasmin Khairani Junaidi Sholat Mufti Hafiz Mustika Fitri Larasati Rika Nofitri Riki Andri Yusda sahren, sahren Santoso Santoso Shaqilla Swita Sarah William Ramdhan