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Sekretariat KESATRIA: Jurnal Penerapan Sistem Informasi (Komputer & Manajemen) Jln. Jendral Sudirman Blok A No. 1/2/3 Kota Pematang Siantar, Sumatera Utara 21127
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Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Published by Sekolah Tinggi Ilmu Komputer Tunas Bangsa
ISSN : -     EISSN : 2720992X     DOI : 10.30645
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering
KESATRIA: Jurnal Penerapan Sistem Informasi (Komputer & Manajemen) adalah sebuah jurnal peer-review secara online yang diterbitkan bertujuan sebagai sebuah forum penerbitan tingkat nasional di Indonesia bagi para peneliti, profesional, Mahasiswa dan praktisi dari industri dalam bidang Ilmu Kecerdasan Buatan. KESATRIA: Jurnal Penerapan Sistem Informasi (Komputer & Manajemen) menerbitkan hasil karya asli dari penelitian terunggul dan termaju pada semua topik yang berkaitan dengan sistem informasi. KESATRIA: Jurnal Penerapan Sistem Informasi (Komputer & Manajemen) terbit 4 (empat) nomor dalam setahun. Artikel yang telah dinyatakan diterima akan diterbitkan dalam nomor In-Press sebelum nomor regular terbit.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 419 Documents
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Sistem Pelayanan Berbasis Web Pada Wedding Organizer Putri Hanastari Ferdiana Kartika Putri; Novita Mariana
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 4, No 1 (2023): Edisi Januari
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v4i1.101

Abstract

A wedding organizer is a service intended to help someone realize their dream wedding from planning to execution. Princess Hanastari's wedding organizer is a wedding service in Grobogan Regency, which provides various types of wedding packages, the ordering process for Putri Hanastari is still manual via WhatsApp and comes directly to Putri Hanastari's place. Therefore it will have an impact on the duration of time for consumers to get schedules, price information, and facilities offered by the owner. To prevent this, it is necessary to build a web-based service system for wedding organizers. The system built uses the PHP Mysql programming language, the waterfall method and the FCFS (First Come First Serve) scheduling algorithm and uses blackbox testing. The creation of a web-based service system for wedding organizers is expected to be able to maximize services so that they are effective and efficient in disseminating the information offered and making it easier for consumers to order wedding packages.
Image Enhancement using Convolutional Neural Network for Low Light Face Detection Antonius Filian Beato Istianto; Gede Putra Kusuma
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 1 (2024): Edisi Januari
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i1.318

Abstract

This research aims to combine the study of face detection with improvement of image quality in low-light conditions. In this research, we introduce a method that combines Convolutional Neural Networks for image processing to enhance face detection performance in low-light conditions. The proposed method involves pre-processing the images using three image enhancement methods: Deep Lightening Network, Deep Retinex Net, and Signal-to-Noise Ratio Aware. Each of these methods is combined with the face detection method, RetinaFace. The experiment is evaluated using the DARKFACE Dataset, and the performance of each combination is assessed using Average Precision (AP). The combination that yields the best AP value will be determined as the best approach for low-light face detection. The best combination, which utilizes Signal to Noise Ratio Aware for image enhancement and RetinaFace for face detection, achieves an AP score of 52.92%. This result surpasses the face detection performance using the original images from the DARKFACE Dataset, which scored 7.12% in AP. Thus, this experiment demonstrates that image enhancement using Convolutional Neural Networks can significantly improve face detection in low-light conditions
Model Kecepatan Seismik 1-Dimensi Pada Wilayah Gempa Bumi Tarutung 2022 Mw 5.8 Resa Idha; Endah P. Sari; Syahrul Humaidi; Andrean V. H Simanjuntak; Umar Muksin
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 4, No 2 (2023): Edisi April
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v4i2.183

Abstract

On October 1, 2022, an earthquake with a magnitude (M) of 5.8 occurred in the Tarutung region, Indonesia, and was associated with an active fault at a depth of 10 km. The earthquake fault with a dextral mechanism is suitable for the pattern of active fault movement in Sumatra in the Northeast - Southwest direction. A total of 170 aftershocks occurred within a week span with magnitude variations of 1.7 – 4.0. In addition, the Tarutung earthquake was felt by the local people with an intensity of IV-VI MMI and caused 1 fatality, 25 injuries, and around 900 houses damaged. Therefore, this study studies the characteristics of seismicity and damage caused by finding an appropriate 1-Dimensional seismic velocity model. The obtained 1-Dimensional speed model has varying values at a depth of 10 km with a speed of ~5.5 km/s and 30 km with a speed of ~7 km/s. The 1-D velocity model obtained has a convergent and unique solution with an RMS value 1.0. Based on ground motion analysis after relocation, it was found that the high PGA and PGV values were in Tarutung. The PGA results reveal a high percentage value of 10% in Tarutung. This is consistent with the damage data and at the same time confirms that Tarutung is in a seismically active area.
Implementasi Algoritma Backpropagation Dalam Memprediksi Harga Bahan Pangan Deni Saputra; M Safii; M Fauzan
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 1, No 4 (2020): Edisi Oktober
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v1i4.37

Abstract

Foodstuffs are raw materials in the form of agricultural, vegetable, and animal products used by the food processing industry to produce a food product. Prices of foodstuffs sometimes rise and fall erratically. The purpose of this research is to predict the price of foodstuffs by using the Backpropagation algorithm. The data used in this study is food price data from 2016 to 2019, originating from the Pusat Informasi Harga Pangan Statistics (PIHPS). This research uses the neural network method of the Backpropagation algorithm, which uses several architectural models, and the results of this test will yield the best accuracy value.
Klasifikasi Varietas Benih Padi Berdasarkan Morfologi dengan Algoritma Random Forest Muhamad Hafidz Ghifary; Enny Itje Sela
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 2 (2024): Edisi April
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i2.371

Abstract

Rice seeds are one of the main elements in agricultural businesses. The choice of type of rice seed planted can influence the quality of the harvest obtained. The large number of varieties of rice seeds with similar shapes makes identifying the type of rice seed an activity that is not easy and requires experts to do. One fairly fast way to identify rice seed varieties is to use machine learning technology. This research will implement machine learning classification algorithms, namely KNN, Naïve Bayes, and Random Forest. Identification of rice seed varieties is carried out based on the morphological features of the seeds. The dataset used is in the form of seed morphological feature values, namely aspect ratio, solidity, circumference, area, area, roundness, circularity and equivalent diameter. Research stages starting from preprocessing, feature extraction, and experimental parameter values were carried out to find the model with the best performance. Feature selection can increase the testing accuracy on KNN and Random Forest models. The test results obtained an accuracy of 78.3% with KNN, 61.7% using Naïve Bayes, and 90% using Random Forest.
Keluhan Pelangan Dan Efektivitas Service Guarantee Di Perusahaan B2b: Dalam Perspektif Tenaga Sales Tantri Yanuar Rahmat Syah; Z Zulhamiadi; R Rojuaniah; Ikramina Larasati Hazrati Havidz; Fadilah Nur Azizah
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 4, No 4 (2023): Edisi Oktober
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v4i4.232

Abstract

This study aims to examine the influence of service guarantee variables, empowerment, responsiveness in a service recovery efforts, customer satisfaction, customer loyalty on service quality. The approach used in this research is a quantitative approach. The sample used in this study were sales employees, amounting to 100 respondents. The research data was obtained from the results of filling out the questionnaire and analyzed using SEM analysis techniques with the help of the PLS SEM program. The results of this study indicate that (1) ) Service Guarantee has a positive and significant effect on service quality; (2) Service guarantee has a positive and significant effect on empowerment; (3) Empowerment has a positive and significant effect on responsiveness in service recovery efforts (4) Empowerment has a positive and significant effect on service quality; (5) Responsiveness in service recovery efforts has a positive and significant effect on service quality; (6) Service quality has a positive and significant effect on customer satisfaction; (7) customer satisfaction has a positive and significant effect on customer loyalty.
Pemanfaatan Software Desain untuk Pembuatan Media Promosi UMKM Kota Medan Simson Panjaitan; Renal Oktra Surbakti; Reynaldi Emit Simanjuntak; Christine Tamba; Komda Saharja
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 2, No 4 (2021): Edisi Oktober
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v2i4.83

Abstract

Promotion media is a means used in supporting promotion activities and the introduction of products or services to the community, so that when doing promotion media can increase the income of a product or service. The purpose of this Scientific Writing is to increase knowledge for people MSMEs of Medan City to create various designs such as banners, pamphlets, logos, and brochures using Adobe Photoshop CS6 Software. From the results of the analysis that the people of MSMEs of Medan City can be given design training to be able to make their own all promotions of products and services using Adobe Photoshop CS6 Software, because their use is easy to understand for beginners and can be developed more optimally according to ability.
Menentukan Tingkat Kesejahteraan Provinsi Kalimantan Tengah Dengan Penerapan Algoritma K-Means Clustering Menggunakan Rapidminer R Rahmahwati
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 4, No 1 (2023): Edisi Januari
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v4i1.123

Abstract

One of the key problems of local governments is poverty, and Central Kalimantan in Indonesia is one of those local governments. Due to the incorrect definition of impoverished households at the time of data collection, the municipal administration has created a number of initiatives and services that assist community welfare, but they have not been deemed functional. This study's objective is to use a clustering method to identify the level of regional poverty. The clustering method, which makes use of RapidMiner's standard data mining stages, was applied in this investigation. This work develops a method that can identify poor areas and categorize them into three groups—low, medium, and high—using a more precise computation approach
Advancing River Water Quality Prediction A Comparative Study of Anomaly Detection Techniques for Optimizing Dissolved Oxygen Level Forecasting Gregorius Airlangga
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 1 (2024): Edisi Januari
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i1.327

Abstract

In the realm of environmental monitoring, particularly river water quality, the study at hand addresses the paramount challenge of accurately predicting dissolved oxygen (DO) levels—a critical indicator of aquatic ecosystem health. This research targets the complexities inherent in environmental datasets, including the presence of anomalies that can skew predictive models, thereby undermining the reliability of DO level forecasts. By applying and critically evaluating advanced anomaly detection methods—One-Class SVM, Isolation Forest, and Autoencoders—the study endeavors to enhance predictive accuracy and address gaps in existing research methodologies. The methodology encompasses data collection, preprocessing, anomaly detection, and evaluation, working with a dataset comprising five indicators across eight monitoring stations. The research process entailed thorough data preparation, ensuring dataset integrity and uniformity. Anomaly detection was meticulously performed, with each method revealing varying outlier detection sensitivities. The One-Class SVM method identified 23 outliers, the Isolation Forest found 38, and the Autoencoders flagged 88. When assessing the impact on model accuracy, reflected by the RMSE, the Isolation Forest method outperformed the others, achieving the lowest RMSE of 0.9668, indicating a more effective anomaly mitigation contributing to a cleaner dataset. In contrast, the Autoencoders, while detecting the most anomalies, yielded the highest RMSE, suggesting a propensity to overfit and misclassify data variations as anomalies. This study illuminates the criticality of selecting suitable anomaly detection methods tailored to the dataset's nuances, emphasizing that the choice profoundly influences predictive model performance. The Isolation Forest's proficiency in this context underscores its potential as a robust method for environmental data analysis, capable of balancing outlier detection accuracy with predictive model precision.
Analisis Data Mining Produk Retail Menggunakan Metode Asosiasi Dengan Menerapkan Algoritma Apriori Mochammad Septa Sandy; Hamzah Setiawan; Uce Indahyanti
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 4, No 2 (2023): Edisi April
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v4i2.174

Abstract

Retail products are businesses that use association techniques that apply a priori algorithms that retrieve datasets from Github in the form of csv taken online that look for the confidence value of these items by having a minimum support value according to these items taken from various countries 4 countries for analysis. The purpose of this research is to find out the pattern of association which aims to find the greatest value taken from 4 countries according to each item using a priori analysis to find out what is related to the data it has as many as 541,910 purchases of retail products by consumers in the form of a dataset that I get the data via online from github in csv form using jypter notebook. The Apriori algorithm is expected to provide decision support between goods purchased jointly by customers. Data Mining is a process that orders one or more learning using Association Rules which serves descriptive data mining which aims to find associative rules between data items. The main step that needs to be in the association rules is to find out how often item combinations appear in the database, which are often referred to as frequent patterns, to obtain a confidence value to find the minimum support value according to each country.

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