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Jurnal Saintekom (Sains dan Teknologi Komputasi)
Published by Universitas Binaniaga Indonesia
ISSN : -     EISSN : 31091350     DOI : https://doi.org/10.36350/jskom
Core Subject : Science,
Computer Science & IT
Jurnal Sainstekom (Jurnal Sains dan Teknologi Komputasi) diterbitkan oleh Lembaga Penelitian, Pengembangan, dan Pengabdian Kepada Masyarakat (LP3M) Universitas Binaniaga Indonesia (UNBIN) untuk mewadahi hasil penelitian dosen, peneliti, mahasiswa Fakultas Informatika & Komputer UNBIN dan dari luar UNBIN. Terbit empat kali setahun, yaitu pada bulan Maret, Juni, September, dan Desember. Pengiriman artikel, serta proses review/peninjauan, tidak dikenakan biaya apa pun (GRATIS). Tujuan dari jurnal ini adalah untuk mempublikasikan hasil penelitian di bidang komputer yang meliputi, (1) Teknik Informatika: Bahasa Pemrograman, Rekayasa Perangkat Lunak, Algoritma (2) Sistem Informasi: Sistem Pendukung Keputusan, Data Mining (3) Teknologi Informasi: Internet of Things, Networking, user experience. Jurnal ini bersifat Open Acces tanpa biaya publikasi (No APC) dan menerapkan proses double-blind peer review.
Arjuna Subject : Ilmu Komputer (semua kategori) - Perangkat Lunak
Articles 60 Documents
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Penerapan Metode Topsis untuk Pemeringkatan Layanan Mobile Banking Berdasarkan Data Transaksi Nasabah Ari Kurniawan; Derman Janner Lubis
Jurnal SAINTEKOM (Sains dan Teknologi Komputasi) Vol 2 No 3 (2026): September 2026
Publisher : Lembaga Penelitian, Pengembangan, dan Pengabdian Kepada Masyarakat (LP3M) – Universitas Binaniaga Indonesia (UNBIN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36350/jskom.v2i3.119

Abstract

Ranking mobile banking services is crucial for banks to determine which services are more strategically valuable, not just frequently used. However, banks have traditionally ranked services solely based on frequency of use, while other aspects such as number of users, average transaction amount, and average transaction costs are also important. This creates a gap because the ranking results are biased. Using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, this study addresses this gap by considering multiple criteria, resulting in more accurate results. The product test results from user validation were declared "Very Feasible" with a 96% achievement percentage. The Spearman rank test yielded a value of 0.43, which falls into the "Moderate" category, indicating that the relationship between the variables is quite significant and influential, but not yet very strong. 
Deteksi Penyakit Kulit Dengan Pendekatan Machine Learning Berbasis Convolutional Neural Network (CNN) Deliya Syafa; Ardin Hersandini
Jurnal SAINTEKOM (Sains dan Teknologi Komputasi) Vol 2 No 3 (2026): September 2026
Publisher : Lembaga Penelitian, Pengembangan, dan Pengabdian Kepada Masyarakat (LP3M) – Universitas Binaniaga Indonesia (UNBIN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36350/jskom.v2i3.120

Abstract

Measles is a contagious disease that is often experienced, especially by children, so it requires early detection to prevent serious complications. This study aims to apply a Convolutional Neural Network (CNN)-based Machine Learning approach to detect measles quickly and accurately. The data used consists of 1,041 skin images (measles and non-measles) obtained from the research dataset. The data is processed through the stages of pre-processing, data augmentation, dataset division (training, validation, testing), model training using the ResNet34 architecture, and performance evaluation with a confusion matrix. The results show that the developed measles detection system has an accuracy of 95%, Precision of 90%, Recall of 100%, and F1-Score of 94.7%. The feasibility test shows that the system is very feasible with a feasibility level of 100% and a user satisfaction level of 100% based on the PSSUQ questionnaire. This study proves that the CNN algorithm with the ResNet34 architecture implemented on the website is able to provide accurate, fast, and effective detection in supporting the early detection of measles.
Penerapan Metode Topsis untuk Menentukan Menu Terfavorit Mie Gacoan Lubuk Buaya Padang Berdasarkan Data ShopeeFood Dhea Zulis Syafitri; Yaslinda Lizar
Jurnal SAINTEKOM (Sains dan Teknologi Komputasi) Vol 2 No 3 (2026): September 2026
Publisher : Lembaga Penelitian, Pengembangan, dan Pengabdian Kepada Masyarakat (LP3M) – Universitas Binaniaga Indonesia (UNBIN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36350/jskom.v2i3.121

Abstract

Digital transformation in the culinary industry has encouraged the utilization of online transaction data as foundation for business decision-making. One of the challenges faced by culinary businesses is determining the most favored menu objectively based on multiple evaluation criteria. This study aimed to implement the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to identify the favorite menu at Mie Gacoan Lubuk Buaya Padang using ShopeeFood data. The study employed three primary criteria, namely the number of sales, price, and costumer preferences. The weighting process was conducted using the Rank Order Centroid  (ROC) method to obtain proportional importance values for each criterion. The research data consisted of five menu alternatives: Mie Hompipa Level 1, Mie Gacoan Level 1, Udang Rambutan. Udang Keju, dan Es Gobak Sodor. The calculation stages included constructing the decision matrix, normalizing the matrix, generating the weighted normalized matrix, determining positives and negative ideal solutions, calculating preference distances, and evaluating the relative closeness value of each alternative.  The results indicated that Udang Keju achieved the highest preference value of 0.933 and was therefore identified as the most favored menu based on the TOPSIS calculation. The findings demonstrate that the TOPSIS method is capable of providing systematic, measurable, and effective decision recommendations in multi-criteria menu evaluation processes.
Penerapan Algoritma Decision Tree C4.5 untuk Pemilihan Tanaman Umbi di Desa Cinangka Dimas Pratama Hariyanto; Muhamad Miftahudin
Jurnal SAINTEKOM (Sains dan Teknologi Komputasi) Vol 2 No 3 (2026): September 2026
Publisher : Lembaga Penelitian, Pengembangan, dan Pengabdian Kepada Masyarakat (LP3M) – Universitas Binaniaga Indonesia (UNBIN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36350/jskom.v2i3.122

Abstract

Farmers often experience difficulties in determining the types of crops that are suitable for current weather conditions, which can lead to improper planting schedules and an increased risk of crop failure. This study aims to develop a crop selection recommendation system based on historical weather data using a machine learning approach. The method employed in this research is supervised learning with the Decision Tree C4.5 algorithm, which is capable of performing data classification and generating predictive models in the form of decision trees that are easy to understand for non-technical users such as farmers. The system is designed to process historical weather data and agricultural environmental characteristics to produce optimal crop recommendations. The Decision Tree C4.5 algorithm is chosen due to its high interpretability and good classification accuracy. With the implementation of this system, it is expected that fa
Penerapan Metode SAW untuk Menentukan Prioritas Barang Inventaris Laboratorium Komputer di Sekolah Menengah Kejuruan Fladimir Aksel Djami; Syafrial Syafrial
Jurnal SAINTEKOM (Sains dan Teknologi Komputasi) Vol 2 No 3 (2026): September 2026
Publisher : Lembaga Penelitian, Pengembangan, dan Pengabdian Kepada Masyarakat (LP3M) – Universitas Binaniaga Indonesia (UNBIN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36350/jskom.v2i3.123

Abstract

A computer laboratory is a key facility supporting learning activities in Vocational High Schools (SMK). High usage intensity increases the risk of equipment damage. A common issue is that the processes of inspection and prioritizing maintenance are still performed manually, which is time-consuming and potentially disrupts the learning process. This study aims to implement the Simple Additive Weighting (SAW) method within a Decision Support System (DSS) to determine maintenance priorities for computer laboratory inventory. The study employs the Research and Development (R&D) method using the Prototype model. The criteria used include the year of purchase, usage intensity, maintenance history, and equipment brand. The results indicate that the SAW method can generate objective inventory maintenance priority recommendations and assist the school in decision-making. Testing results show a system feasibility rating of 100% by experts and a user acceptance rate of 92.2%, confirming that the system is highly suitable for use as a decision support tool for computer laboratory inventory management.
Penerapan Metode K-Nearest Neighbor untuk Prediksi Penjualan Sparepart Mobil pada Gudang Marketplace Indah Nur Qoriah; Leny Tritanto Ningrum
Jurnal SAINTEKOM (Sains dan Teknologi Komputasi) Vol 2 No 3 (2026): September 2026
Publisher : Lembaga Penelitian, Pengembangan, dan Pengabdian Kepada Masyarakat (LP3M) – Universitas Binaniaga Indonesia (UNBIN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36350/jskom.v2i3.124

Abstract

This study focuses on predicting car spare parts sales in a marketplace warehouse by employing the K-Nearest Neighbor (K-NN) algorithm. K-NN is utilized as an effective data mining technique for classification in order to construct an accurate predictive model. The dataset used in this research consists of key attributes, including spare part name, sales volume, quantity, price, condition, and brand. A web-based prototype was developed using PHP, enabling accessibility across devices and providing itemset predictions through Euclidean distance analysis. To assess model performance, evaluation was carried out using a confusion matrix, which produced an accuracy of 72%, precision of 86%, and recall of 100%. Furthermore, a feasibility test of the system achieved a perfect score of 100%, while user testing resulted in 83.03%, both of which indicate that the system is highly feasible and user-friendly. The research findings confirm that the K-NN method can deliver effective and reliable predictions of car spare parts sales. Beyond its theoretical contribution in expanding insights into K-NN applications, this study also offers practical value by supporting inventory management efficiency and assisting company management in making strategic business decisions.
Deteksi Dini Penyakit Preeklampsia pada Ibu Hamil dalam Mendukung Penegakan Diagnosis Menggunakan Pendekatan AlgoritmaK-Nearest Neighbor (KNN) Jihan Anindia Maharani; Hardi Jamhur
Jurnal SAINTEKOM (Sains dan Teknologi Komputasi) Vol 2 No 3 (2026): September 2026
Publisher : Lembaga Penelitian, Pengembangan, dan Pengabdian Kepada Masyarakat (LP3M) – Universitas Binaniaga Indonesia (UNBIN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36350/jskom.v2i3.125

Abstract

Preeclampsia is a pregnancy complication that may increase the risk of maternal and fetal mortality if it is not detected and managed early. At the primary healthcare level, screening still relies on healthcare workers’ assessment of examination results. Therefore, a data-based approach is needed to support the identification of preeclampsia risk. This study aims to develop a website-based prototype that applies the K-Nearest Neighbor (KNN) algorithm to support the early detection of preeclampsia in pregnant women. The research method used was Research and Development (R&D) with a Prototype model, while data processing followed the CRISP-DM stages. The data were obtained from Puskesmas and consisted of age, parity, height, weight, body mass index, systolic blood pressure, diastolic blood pressure, Mean Arterial Pressure (MAP), random blood glucose, and urine protein. The modeling results showed that K=3 achieved an accuracy of 99.81%, precision of 100.00%, recall of 98.97%, and F1-score of 99.48%. System testing obtained 95% in Black Box Testing, 100% in White Box Testing, and 82.8% in user testing using the Post-Study System Usability Questionnaire (PSSUQ). The developed prototype is expected to help healthcare workers conduct more objective screening and support the diagnostic process at Puskesmas.
Penerapan Metode Topsis dalam Pemilihan Penyedia Barang di Kecamatan Berdasarkan Kriteria E-Katalog V6 Khalif Supwiro; Rajib Ghaniy
Jurnal SAINTEKOM (Sains dan Teknologi Komputasi) Vol 2 No 3 (2026): September 2026
Publisher : Lembaga Penelitian, Pengembangan, dan Pengabdian Kepada Masyarakat (LP3M) – Universitas Binaniaga Indonesia (UNBIN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36350/jskom.v2i3.126

Abstract

Procurement activities at the sub-district level play an important role in supporting the effectiveness of public service delivery. However, the selection of suppliers through the E-Catalogue version 6 (V6) system is still often carried out manually, making it difficult for the Activity Technical Implementation Officers (PPTK) to determine the best alternative quickly and objectively. This study aims to develop a web-based Decision Support System (DSS) by applying the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to provide measurable and transparent supplier selection recommendations. Data were collected through interviews with PPTK officers in Sukaraja Sub-district and by gathering laptop supplier data from the E-Catalogue of the National Public Procurement Agency (LKPP). The system was developed using a prototyping approach with PHP programming language and a MySQL database. The evaluation criteria include price, conformity of specifications with the budget (DPA), Domestic/Local Content Value (TKDN/PDN), MSME status, and supplier location. System evaluation was conducted in three stages: expert Testing, user Testing, and result Testing. The expert Testing showed a system feasibility level of 89% (classified as highly feasible), while the user Testing using the Post-Study System Usability Questionnaire (PSSUQ) produced an average score of 6.02 out of 7 (86.01%), categorized as very good. Meanwhile, the Hit Ratio test achieved a value of 20%, indicating partial consistency between system recommendations and manual PPTK decisions. The findings demonstrate that implementing the TOPSIS method in the Decision Support System can generate systematic, objective, and relevant supplier rankings, thereby improving efficiency, transparency, and accountability in the procurement process at the sub-district level.
Penerapan Simple Additive Weighting (SAW) untuk Seleksi Penentuan Pemilihan Indekos (Kos) di Sekitar Perkantoran Muhamad Sopian Aryanto; Anggra Triawan; Alam Supriyatna
Jurnal SAINTEKOM (Sains dan Teknologi Komputasi) Vol 2 No 3 (2026): September 2026
Publisher : Lembaga Penelitian, Pengembangan, dan Pengabdian Kepada Masyarakat (LP3M) – Universitas Binaniaga Indonesia (UNBIN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36350/jskom.v2i3.127

Abstract

The selection of boarding houses (indekos) for workers living away from home has become increasingly important due to the wide variety of available options with different prices and facilities. However, the absence of organized and up-to-date information makes the decision-making process inefficient and time-consuming. This study aims to develop a Decision Support System (DSS) using the Simple Additive Weighting (SAW) method to assist workers in choosing the most suitable boarding house around office areas. The SAW method works by calculating the weighted sum of performance ratings for each alternative against multiple criteria such as price, location, and facilities. The system was developed using PHP (CodeIgniter framework) and a MySQL database. Evaluation results showed overall effectiveness of 96%, user satisfaction of 89%, information quality of 91%, interface quality of 87%, and reliability value of 0.79, indicating that the application is highly feasible for use.
Penerapan Metode Simple Additive Weighting (SAW) untukRekomendasi Wali Kelas Ideal Tingkat Sekolah Dasar Rizqi Nur Robby; Adiat Pariddudin; Binanda Wicaksana
Jurnal SAINTEKOM (Sains dan Teknologi Komputasi) Vol 2 No 3 (2026): September 2026
Publisher : Lembaga Penelitian, Pengembangan, dan Pengabdian Kepada Masyarakat (LP3M) – Universitas Binaniaga Indonesia (UNBIN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36350/jskom.v2i3.128

Abstract

In the research conducted at SDN Cibalagung 3 in the previous business process, generally every determination of the ideal homeroom teacher was not effective and not right because it still used the standard method which calculated it only by adding all the criteria and averaging it, and the objectives of this study are: 1) Getting a more effective process in determining the homeroom teacher because of the recommendation of the ideal homeroom teacher, 2) Getting accuracy in determining the homeroom teacher because of the recommendation of the ideal homeroom teacher, 3) Developing a prototype of the ideal homeroom teacher recommendation application, 4) Measuring the level of accuracy and effectiveness of the application of the Simple Additive Weighting (SAW) method for the recommendation of the ideal homeroom teacher. The implementation of this development uses the Prototype method, the steps are: 1) Collecting and analyzing needs, 2) Carrying out rapid design, 3) Building a prototype, 4) Evaluation is carried out by users on the prototype, 5) Changes to the design of the prototype, 6) If the prototype does not match what the user expects, repeat step 5, and 7) If the user is satisfied with the prototype, the development of product engineering can begin. Data collection in this study uses documentation. The results of the development show that: 1) The application created can produce optimal solutions by getting ideal homeroom teacher recommendations, 2) The application is able to help StakeHolders in making decisions, 3) The application is able to print recommendation reports, and 4) the application developed is categorized as "Very Eligible" because a 100% eligibility percentage was obtained based on testing on system experts and material experts. Meanwhile, based on the results of testing on users, the results were 82.2% and included in the "Very Eligible" category, and the correlation/suitability coefficient value that has been obtained with a value of 0.57 is included in the high category referring to the Spearman rank table.

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