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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Prosiding Snastikom PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Jurnal Ilmu Komputer dan Sistem Informasi Journal of Computer Science Artificial Intelligence and Communications
Rismayanti
Universitas Harapan Medan
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Library & Information Science Mechanical Engineering

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Implementasi Data Mining Pada Hasil Penjualan Makanan Beku Menggunakan K Means Clustering Nenna Irsa Syahputri; Siti Sundari; Rismayanti
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 2 No. 2 (2023): Mei 2023
Publisher : LKP Unity Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70340/jirsi.v2i2.54

Abstract

Maintaining inventory stock so that there are no empty items is one way to maintain customer satisfaction. In today's competitive business world, we are required to always develop our business in order to survive in the competition, especially in sales competition, it requires entrepreneurs to find a pattern that can increase sales and marketing within the company, one of which is by utilizing sales data. Applying clustering data mining techniques so that it can help NCekma Frozen stores in determining strategies for determining frozen food stocks using the K Means algorithm.
Penerapan Data Mining dengan Metode Algoritma Apriori untuk Menentukan Pola Pembelian Froozen Food Siti sundari; Rismayanti; Khairunnisa
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 2 No. 2 (2023): Mei 2023
Publisher : LKP Unity Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70340/jirsi.v2i2.55

Abstract

Maintaining inventory stock so that there are no empty items is one way to maintain customer satisfaction. In today's competitive business world, we are required to always develop our business in order to survive in the competition, especially in sales competition, it requires entrepreneurs to find a pattern that can increase sales and marketing within the company, one of which is by utilizing sales data. Applying clustering data mining techniques so that it can help NCekma Frozen stores in determining strategies for determining frozen food stocks using the K Means algorithm.
APLIKASI ANAGLYPH 3D TATA CARA SHOLAT DAN DOA BERBASIS LIGHT VIRTUAL REALITY Ahmad Muammar Lubis; Sumi Khairani; Rismayanti
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 3 No. 2 (2024): Mei 2024
Publisher : LKP Unity Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70340/jirsi.v3i2.121

Abstract

Prayer is the second pillar of Islam and it is the pillar that is emphasized most after the two sentences of the shahada. Prayer is the connection between a servant and his Lord. Prayers are of two types, namely prayers of worship and prayers of supplication. Allah's closeness to His servants is divided into two types, namely; the closeness of His knowledge to every creature and the closeness to His servants in giving them every request, help and taufik. Learning prayer movements and prayers should be taught from an early age (children). Guidance from parents and teachers is the most important way to provide learning media. So far, conventional learning in the form of books makes children bored, so creativity or interactive learning methods are needed, one of which is multimedia-based learning.
Implementasi Metode K-Nearest Neighbor Untuk Klasifikasi Penyakit Tanaman Mentimun Pada Citra Daun Ratna Indah Juwita Harahap; Sumi Khairani; Rismayanti
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 3 No. 2 (2024): Mei 2024
Publisher : LKP Unity Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70340/jirsi.v3i2.123

Abstract

Cucumber is a vegetable that is widely consumed by Indonesian people. However, cucumber plants are susceptible to disease attack which causes substantial yield loss. Examples of disease in cucumber plants are downy mildew, powdery mildew, and cucumber mozaic virus. This disease can be recognized visually because it has a characteristic color and texture. Through an image, information can be learned about the cucumber plant disease. This study aims to build a disease classification system on cucumber leaf images so that it can provide information on the type of disease. The application of the system consisting of pre-processing, feature extraction, classification, and evaluation stages. The pre-processing stages resizes the RGB image and then converts it to Grayscale. The feature extraction stage uses the GLCM (Gray Level Co-Occurence) method. The classification stage uses the K-NN (K-Nearest Neighbor) algorithm. Evaluation stage is a confusion matrix. The results of the cucumber leaf disease classification test used the K-Nearest Neighbor algorithm, produced the best accuracy value by using the neighborhood value k=1 reaching 90%.
Pengembangan Media Pembelajaran Pengenalan Jenis Warna Pada Anak Dengan Metode Research and Development Berbasis Android Haida Dafitri; Rismayanti; Asri Tamara
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 2 No. 2 (2023): September 2023
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v2i2.121

Abstract

Usia dini merupakan tahap awal terpenting dalam pertumbuhan dan perkembangan kehidupan manusia. Kali ini, menampilkan beberapa tahapan penting yang menjadi dasar kehidupan anak selanjutnya. Salah satu periode yang menjadi ciri anak usia dini adalah Golden Age. Anak usia dini memiliki batasan dan ciri khas tertentu, serta berada dalam proses perkembangan yang sangat pesat. Di dalam belajar tentunya sering ditemui bahwa anak-anak cenderung bosan dan malas dalam belajar, rasa ngantuk dan tidak semangat ini dikarenakan cara belajar yang begitu monoton dimana anak hanya membaca dan mendengarkan penjelasan dari Bapak atau Ibu guru. Salah satu materi mengenal warna sangat diperlukan oleh seorang anak sebelum memasuki pra sekolah, karna kemampuan mengenal warna akan berhubungan dengan kemampua anak untuk berfikir secara logis. Seiring bertambahnya tahun dengan zaman yang sudah modren ini maka manusia tidak akan dapat menghindar dari perkembangan teknologi yang sudah merambat kepada semua aspek kehidupan. Dengan perkembangan teknologi yang sangat cepat maka tidak akan dapat lepas dari kemajuan sumber daya manusianya yang semakin berkualitas. Perkembangan ilmu pengetahuan, teknologi informasi dan komunikasi yang sangat cepat ini diharapkan dapat membantu meningkatkan sarana dalam proses pembelajaran.
K-Means and Fully Connected Neural Network for Child Nutritional Status Classification Rismayanti; Sumi Khairani
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16400

Abstract

Stunting remains a persistent child nutrition problem because delayed growth is closely related to long-term health, cognitive, and productivity risks. Manual interpretation of anthropometric measurements using the World Health Organization Z-score standard is clinically valid, yet it becomes inefficient and error-prone when routine records are processed in large numbers. This study develops a child nutritional status classification model by combining K-Means clustering and a fully connected neural network for early identification of stunting, underweight, and wasting. The dataset consisted of toddler anthropometric records from 2021-2024 with sex, age, body weight, and body height attributes. The data were cleaned, standardized, transformed into Z-score indicators, and grouped into 27 clusters representing possible combinations of nutritional status profiles. Cluster membership was then used with Zlen, Zwei, and Zwfl features in a multi-head fully connected neural network. Evaluation on 82 held-out samples showed accuracy values of 91.46% for stunting, 93.90% for underweight, and 98.78% for wasting. Weighted precision, recall, and F1-score were consistently high across the three outputs, while the training curves indicated stable learning without strong overfitting. The proposed hybrid model improves the reliability of child nutrition classification and can support a web-based decision support system for data-driven nutritional screening and intervention planning.
Utilization of Sales Data Analysis for Product Recommendation Systems in E-Commerce Using the Apriori Algorithm Muhammad Noor Hasan Siregar; Furqan Khalidy; Rismayanti; Khairunnisa
Journal of Computer Science, Artificial Intelligence and Communications Vol 1 No 2 (2024): November 2024
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/jocsaic.v1i2.17

Abstract

The rapid development of e-commerce has significantly increased the volume of sales transactions and customer interaction data. This presents an opportunity for businesses to leverage data mining techniques to extract valuable insights that support decision-making processes. One such application is the development of product recommendation systems, which play a crucial role in enhancing customer satisfaction and driving sales. This research focuses on utilizing sales transaction data to build a product recommendation system using the Apriori algorithm, a well-known method for association rule mining. The study begins with the collection and preprocessing of transaction data from an e-commerce platform. Through the application of the Apriori algorithm, frequent itemsets are identified, and association rules are generated based on specified support and confidence thresholds. These rules reveal purchasing patterns and relationships between products that are frequently bought together. The system then uses these patterns to recommend relevant products to users, aiming to improve cross-selling opportunities and personalize the shopping experience. The results demonstrate that the Apriori-based recommendation model is effective in identifying meaningful product combinations and can be implemented as a lightweight, interpretable alternative to more complex machine learning methods. Furthermore, the system helps e-commerce businesses optimize inventory management and marketing strategies by understanding customer buying behavior. This research concludes that the integration of the Apriori algorithm into recommendation systems provides tangible benefits for e-commerce platforms seeking data-driven personalization solutions.
Co-Authors Ahmad Muammar Lubis Asri Tamara Dharmawati Dian Alfa Ridho Elvira Hanum Nst Ema Wardiana Emma Yurispa Siregar Furqan Khalidy Haida Dafitri Harahap, Herlina Hasibuan, Ade Zulkarnain Khairunnisa, Khairunnisa Muchriza Akbar Bancin Muhammad Fitra Hanafiah Muhammad Irsan Muhammad Noor Hasan Siregar Nur Ainun Nur Wulan Ratna Indah Juwita Harahap Siti Sundari Sumi Khairani Surya Ramadan Hasibuan Syahputri, Nenna Irsa Victor Alwi Hura Vira Anjani Yessi Fitri Annisah Lubis Yuyun Dwi Lestari