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INDONESIA
KOMPUTIKA - Jurnal Sistem Komputer
Published by Universitas Komputer Indonesia
ISSN : 22529039     EISSN : 26553198     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
Jurnal Ilmiah KOMPUTIKA adalah wadah informasi berupa hasil penelitian, studi kepustakaan, gagasan, aplikasi teori dan kajian analisis kritis di bidang kelimuan bidang Sistem Komputer.
Arjuna Subject : -
Articles 218 Documents
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Rekomendasi Pemilihan Bahan Bacaan Pengunjung Perpustakaan Menggunakan Metode K-Means Clustering harnelia, harnelia; Panaungi, Fajar; Abbas, Muhammad Akram; Saputra, Rizal Adi
Komputika : Jurnal Sistem Komputer Vol. 14 No. 1 (2025): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v14i1.13298

Abstract

This research was conducted at the Modern Library of Kendari City to classify visitors' reading interests using the k-means clustering method. The classification aims to provide reading recommendations that match each visitor group's interests. The study uses book lending data collected from library visitors over the past four years. The clustering process implements the k-means algorithm, grouping data based on the nearest distance to cluster centers. This method resulted in three main clusters: cluster 0 with low reading interest, cluster 1 with moderate reading interest, and cluster 2 with high reading interest. This study contributes by developing a new approach for the Modern Library of Kendari City in managing book collections and recommending readings based on visitor interest groups. The clustering visualization provides insights into reading interest distribution, which helps the library make decisions about reading material provision. The cluster analysis shows different borrowing patterns and book preferences. This research is expected to help the library improve its services and visitor satisfaction through providing book collections that match each group's reading interests. Keywords – Book Recommendations; Clustering; Library; Machine Learning; Reading Interest
Rancang Bangun Miniatur Sistem Monitoring Stock Barang Menggunakan Sensor Load Cell Dan Sensor Ultrasonik Berbasis IoT Hamuda, Hayadi
Komputika : Jurnal Sistem Komputer Vol. 14 No. 1 (2025): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v14i1.13721

Abstract

Bisnis apapun yang ingin beroperasi secara efisien harus memiliki pemahaman yang tepat tentang persediaan stok barang di gudang. Pemeriksaan stok barang di setiap gudang perusahaan adalah hal yang wajib dilakukan. Ketika tingkat persediaan di perusahaan manufaktur rendah, stock opname dapat membantu perusahaan mengisi kembali persediaan atau membuat pesanan baru dengan segera. Hal ini juga dapat membantu gudang memperkirakan berapa banyak persediaan barang atau bahan baku yang mereka miliki. Namun, secara umum, setiap bisnis melakukan penghitungan stok secara manual, yang mengharuskan karyawan menghitung setiap barang saat dibawa ke gudang satu per satu. Hal ini tentu saja membutuhkan banyak tenaga dan waktu. Dengan menggunakan alat ini, penghitungan stok barang dapat dilakukan dengan lebih cepat dengan jumlah karyawan yang lebih sedikit. Pengoperasian alat ini mengandalkan penggunaan sensor ultrasonik dan load cell yang masing-masing dapat mengidentifikasi persediaan produk. Data yang dikumpulkan setiap sensor akan diteruskan ke NodeMCU ESP8266 untuk diproses agar dapat dikonversi. Hasil pengujian menunjukkan bahwa ke dua sensor berbasis IoT yang digunakan dalam sistem pemantauan stok dapat menampilkan data secara realtime yang dapat dilihat secara online. Kesalahan pembacaan sensor load cell yang dikonversi ke dalam unit (ea) adalah 0%, sedangkan kesalahan pembacaan sensor ultrasonik yang dikonversi ke dalam volume (ml) adalah 5,5%. Kata Kunci - Stock opname, Sensor Load Cell, Sensor Ultrasonik HC-SR04, NodeMCU ESP8266, IoT.
Teknologi Internet of Things untuk Meningkatkan Mobilitas Penyandang Tunanetra Tanpa Batasan Ruang dan Waktu Ramadhani, Muhammad Tachyul Qulub; Imam Purwanto , Imam Purwanto
Komputika : Jurnal Sistem Komputer Vol. 14 No. 1 (2025): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v14i1.14289

Abstract

This system uses NodeMCU ESP8266 and ESP32 CAM to create a cane for the visually impaired. The HC-SR04 ultrasonic sensor detects objects within a distance of 50 cm and activates a buzzer as a warning. The buzzer will ring if the yellow guidance route is not identified by the TCS3200 color sensor. When the water level hits a certain point, the water sensor sounds the buzzer to warn of flooding. Locations are tracked by the GPS module, and alerts with a link to Google Maps are sent out with the findings. The camera module is used for real-time monitoring through the Blynk platform, allowing users to see the surrounding conditions. This research tests the effectiveness of the tool by measuring the accuracy of GPS signals in various locations and the quality of video from the camera under different conditions. To enhance the effectiveness of the tool, it is recommended to add a DC motor for automatic braking, touch sensors, advanced cameras, and an MP3 module for sound output. The research method includes design and construction as well as data collection from the analysis results during testing. This system is designed to facilitate the travel of visually impaired individuals by providing early warnings about obstacles, going off course, or puddles Keywords - Camera; Color; Distance; Location; Water
Implementasi Deteksi Gerakan Tangan untuk Sistem Interaktif Kios menggunakan Metode Long Short-Term Memory (LSTM) Kurniasari, Arvita Agus; Wiryawan, I Gede; Dewi Puspitasari, Pramuditha Shinta; Rizaldi, Taufiq; Putra, Dhony Manggala
Komputika : Jurnal Sistem Komputer Vol. 14 No. 1 (2025): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v14i1.14914

Abstract

Deaf individuals in Indonesia face challenges in using voice-based technology. This study aims to develop an interactive kiosk system utilizing hand gesture detection based on Long Short-Term Memory (LSTM) to provide a more inclusive solution. The research process includes collecting hand gesture datasets using MediaPipe, splitting the dataset into training and testing data with a 75:25 ratio, and training the model using a Learning Rate Scheduler. The model architecture is designed to capture patterns from keypoint data by optimizing the use of dropout layers and the softmax activation function. The evaluation shows that the model achieves an accuracy of 90.22% on the test data, with an average precision of 91%, recall of 89%, and F1-score of 90%. The trial results also demonstrate consistent performance for simple gestures, while accuracy decreases for complex gestures and greater distances. This research provides a significant contribution to enabling voice-free interaction, particularly for deaf individuals, by integrating LSTM technology into interactive kiosk systems.
Penerapan Komputasi Paralel dalam Pengembangan Model Random Forest: Untuk Memprediksi Coral Bleaching Kesuma, M Salman; Yudha, Apri Anggara; Nugroho, Eddy Prasetyo
Komputika : Jurnal Sistem Komputer Vol. 14 No. 1 (2025): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v14i1.14982

Abstract

Coral bleaching is a critical environmental issue caused by environmental stressors, such as rising sea temperatures, which result in the loss of algae symbiosis within corals. However, predicting coral bleaching remains challenging due to the complexity of environmental conditions, the uncertainty of contributing factors, and the limited availability of accurate and consistent data. Additionally, managing large datasets and ensuring efficient training of predictive models with complex datasets pose significant challenges. This study explores the application of parallel computing in developing a predictive Random Forest model to forecast coral bleaching events based on environmental data, including sea surface temperature (SST), sea surface temperature anomalies (SSTA), depth, and location coordinates. Parallel computing is employed to enhance efficiency in training the model by utilizing multi-core processors, significantly reducing execution time. The results demonstrate that the model achieves a prediction accuracy of 95.19% with an R-squared value of 0.685. The application of parallel computing also shows a reduction in computation time, although not always linear due to the overhead associated with task management. This research is expected to support coral reef conservation efforts by providing a faster and more accurate predictive model. Keywords – Parallel Computing; Random Forest; Coral Bleaching.
Parallel Computing pada Clustering K-Means untuk Analisis Keketatan Program Studi SNBT 2023 Firdaus, Alif Faturahman; Fahriza Fitriani, Azzahra; Prasetyo Nugroho3, Eddy
Komputika : Jurnal Sistem Komputer Vol. 14 No. 1 (2025): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v14i1.14983

Abstract

This study aims to analyze the competitiveness of study programs in the 2023 SNBT dataset using the Knowledge Discovery in Databases (KDD) method and the K-Means Clustering algorithm. The competitiveness of study programs is measured by the ratio between the number of applicants and available slots, reflecting the level of competition and popularity of the programs. Two main issues are addressed: the urgency of data-driven decision-making for formulating effective student admission policies and the lengthy execution time on large datasets such as the 2023 SNBT data, which includes thousands of study programs with complex variables. The number of clusters was determined using the elbow method, dividing the data into three categories: low, medium, and high. Clustering evaluation was conducted using the silhouette score metric, revealing that Cluster 0 (low) demonstrated the best quality with the highest silhouette score. To accelerate the analysis process, parallel computing was implemented using the joblib, scikit learn and multiprocessing library, significantly reducing execution time compared to conventional methods. With an average silhouette score of 0.684816, the results indicate good clustering quality. These findings provide valuable insights for universities in understanding the competitiveness patterns of study programs and support the development of more effective and efficient data-driven student admission policies.
Sistem Monitoring Kapasitas dan Kualitas Air dengan Metode SWAT (Smart Water Meter) menggunakan Protokol Lora berbasis IoT Bakti, Very Kurnia; Abdul Basit; Rais; Wiwit Suryanto
Komputika : Jurnal Sistem Komputer Vol. 14 No. 1 (2025): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v14i1.15051

Abstract

Penyedia Air Minum dan Sanitasi Berbasis Masyarakat atau biasa disebut Pamsimas merupakan program pemerintah, Pamsimas Tirta Abadi di Desa Rangimulya Kecamatan Warureja Kabupaten Tegal dengan jumlah penduduk sekitar 3104 Jiwa, Pamsimas Tirta Abadi memiliki 120 pelanggan dan keberadaan Pamsimas sangat penting bagi masyarakat. Dalam menghitung penggunaan air bersih pengelola Pamsimas masih menggunakan meteran air analog, banyak potensi kesalahan dalam pencatatan penggunaan air.  Penerapan  IoT  bisa menjadi solusi, sensor yang memungkinkan untuk menghitung penggunaan air adalah sensor waterflow dipadukan dengan ESP32 dan modul LoRa. dari beberapa teknologi tersebut dibangun sebuah sistem meteran air cerdas atau smart water meter dengan tujuan meningkatkan pelayanan dan pendapatan.  Metode pada smart water meter ini membaca kondisi air dengan penggunaan sensor, sensor waterflow membaca nilai kubikasi dengan konstanta 9,5 dengan nilai 5,9 dari uji pembacaan air 1 Liter, nilai baca 4.00 pada sensor PH  memiliki nilai pH mulai dari 0 hingga 7, sensor turbidity mendeteksi tingkat kejernihan / kekeruhan yang diperoleh Sensor Turbidity Output (v): 4.42 dengan NTU: 0.52. GPS sensor mengirimkan latitude dan longitude pada gateway untuk melihat titik pelanggan, Data semua sensor yang terbaca dikirim dari node kepaa gateway dengan modul lora pada frekuensi 915E6 dan ditampilkan pada interface berbasis website menggunakan modul internet ESP32. Kata Kunci: PAMSIMAS, IoT, Lora, waterflow, PH, Turbidity, GPS
Prediksi Kabut Menggunakan Recurrent Neural Network dengan Attention Mechanism di Bandara Ruteng Wiujianna, Atri; Pribadi, Feddy Setio; Djuniadi, Djuniadi; Sunarno, Sunarno; Iqbal, Iqbal
Komputika : Jurnal Sistem Komputer Vol. 14 No. 1 (2025): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v14i1.15380

Abstract

Fog phenomena pose a significant challenge in aviation operations, particularly in regions with complex topography such as Ruteng Airport. Thick fog can reduce visibility and increase flight safety risks. This study aims to develop a deep learning-based fog prediction model by comparing the performance of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) enhanced with Attention Mechanism (AM). The dataset consists of 61,187 entries, covering hourly recorded weather parameters over the past ten years (2013–2023). The experimental results show that the addition of Attention significantly improves model performance. The RNN+Attention model emerges as the best-performing model with an accuracy of 0.9981, precision of 0.7755, recall of 0.76, and F1-score of 0.7677, along with the lowest number of False Positives. Meanwhile, the LSTM+Attention model excels in reducing False Negatives, making it suitable for systems prioritizing comprehensive fog detection. Models without Attention demonstrate perfect recall (1.00), but their low precision indicates overfitting. Overall, the integration of the Attention Mechanism enhances the balance between recall and precision and improves model reliability in handling data imbalance. The contribution of this research is that it can serve as a reference for future studies in developing artificial intelligence-based weather prediction models, particularly in addressing fog phenomena. Keywords – Attention Mechanism; Long Short-Term Memory; Fog Prediction; Recurrent Neural Network
Comparison Of Lung Cancer Classification Using Decision Tree And Random Forest: Perbandingan Klasifikasi Penyakit Kanker Paru-Paru Menggunakan Decision Tree Dan Random Forest Adawiyah, Rabiatul; Cahya Julia Kartikasari, Dwi
Komputika : Jurnal Sistem Komputer Vol. 14 No. 1 (2025): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v14i1.15501

Abstract

Lung cancer is the leading cause of cancer-related deaths across various age groups, with risk factors such as smoking, air pollution, and chronic diseases. Lung cancer is characterized by the uncontrolled growth of cells in lung tissue, which can spread to other organs through metastasis. Machine learning-based classification can assist in the early detection of this disease. This study compares the Decision Tree and Random Forest methods in classifying lung cancer using a dataset containing seven attributes and 1,010 data entries. Missing values were handled using mode imputation. Feature importance analysis with Random Forest identified Coughing, Chronic Disease, Smoking, and Shortness of Breath as the most influential features in classification. The classification results showed that Decision Tree without feature selection achieved an accuracy of 64.85%, higher than Random Forest, which reached only 52.62%. After feature selection, Decision Tree accuracy decreased to 55.94%, while Random Forest experienced a slight decline to 52.47%. These findings indicate that Decision Tree is more effective in capturing data patterns without feature selection, whereas Random Forest tends to be less optimal with relatively small datasets. Keywords – Machine Learning; Classification; Feature Importance; Entropy; Gain.
Media Pembelajaran Huruf dan Angka dalam Bahasa Isyarat Dwi Rahmatya, Myrna; Fajar Wicaksono, Mochamad; Ramadhanty, Fani; Tasya Kamila, Syahla; Maharani Iskandar, Meita
Komputika : Jurnal Sistem Komputer Vol. 14 No. 1 (2025): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v14i1.15534

Abstract

Orang yang kehilangan kemampuan pendengaran (tunarungu) memerlukan bahasa isyarat sebagai alat bantu dalam berkomunikasi. Selain diperlukan oleh tunarungu, bahasa isyarat juga dapat dipelajari oleh siapapun. Tujuan dari penelitian ini, yaitu membangun sebuah media pembelajaran yang dapat membantu dalam mempelajari bahasa isyarat, khususnya huruf-huruf alfabet dimulai dari A-Z dan angka dimulai dari 0-9. Metode pengembangan sistem yang digunakan adalah waterfall, mulai dari requirements, design, implementation, dan testing. Hasil penelitian berupa media pembelajaran yang akan memandu pengguna untuk memeragakan huruf ataupun angka dalam bahasa isyarat pada mode belajar. Setelah itu, pengguna dapat berlatih pada mode latihan, yaitu dengan memeragakan kode bahasa isyarat dan sistem akan memberikan feedback berupa suara. Tak hanya mode belajar dan latihan, sistem juga menyediakan mode soal. Dengan mode soal, pengguna dapat menguji pemahamannya seputar huruf dalam bahasa isyarat. Sistem yang dibangun diuji dengan menggunakan metode black-box. Berdasarkan hasil pengujian, sistem dapat mengenali bahasa isyarat baik huruf maupun angka yang diperagakan dengan baik. Sistem juga dapat mengoreksi saat berada dalam mode soal. Kata Kunci – Media Pembelajaran; Bahasa Isyarat; Tunarungu; Media Pembelajaran Bahasa Isyarat; Bahasa Isyarat Huruf dan Angka. 

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