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Tri A. Sundara
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The Indonesian Journal of Computer Science
Published by STMIK Indonesia Padang
ISSN : 25497286     EISSN : 25497286     DOI : https://doi.org/10.33022
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering
The Indonesian Journal of Computer Science (IJCS) is a bimonthly peer-reviewed journal published by AI Society and STMIK Indonesia. IJCS editions will be published at the end of February, April, June, August, October and December. The scope of IJCS includes general computer science, information system, information technology, artificial intelligence, big data, industrial revolution 4.0, and general engineering. The articles will be published in English and Bahasa Indonesia.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,114 Documents
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Analisa Pengaruh Jalur Penerimaan Mahasiswa Baru Terhadap Hasil Akademik Mahasiswa Menggunakan Metode Clustering K-Means Yang, Agita Rindri; Rollastin, Boy; Riyadi, Muhammad
The Indonesian Journal of Computer Science Vol. 12 No. 1 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i1.3103

Abstract

Setiap tahun perguruan tinggi melakukan penerimaan mahasiswa baru untuk menyaring calon mahasiswa baru yang berkualitas. Data penerimaan mahasiswa baru tersebut semakin bertambah setiap tahunnya dan hanya tersimpan sebagai arsip dalam basis data perguruan tinggi. Padahal data-data tersebut dapat diolah dengan menggunakan teknik data mining untuk mendapatkan informasi tertentu yang bermanfaat bagi perguruan tinggi. Penelitian ini bertujuan memanfaatkan data penerimaan mahasiswa baru untuk mengetahui pengaruh jalur penerimaan mahasiswa baru terhadap hasil akademik mahasiswa selama mengikuti perkuliahan dengan menggunakan algoritma K-Means Clustering. Data yang digunakan adalah data penerimaan mahasiswa baru pada Program Studi Sarjana Terapan Teknik Elektro, Program Studi Sarjana Terapan Teknologi Rekayasa Perangkat Lunak, dan Program Studi Sarjana Terapan Teknik Mesin dan Manufaktur Polman Negeri Babel Tahun Akademik 2021/2022 dan data nilai semester ganjil TA 2021/2022. Jumlah data yang digunakan sebanyak 179 data dari Program Studi Sarjana Terapan Teknik Elektro, Program Studi Sarjana Terapan Teknologi Rekayasa Perangkat Lunak, dan Program Studi Sarjana Terapan Teknik Mesin dan Manufaktur. Hasil dari penelitian menunjukkan bahwa K-Means Clustering mengelompokkan data menjadi 3 cluster di setiap kategori jalur penerimaan mahasiswa baru. Hasil cluster tersebut menunjukkan bahwa jalur SNMPTN menghasilkan 48% mahasiswa memiliki hasil akademik 10 terbaik di kelasnya, sementara itu 60% mahasiswa dari jalur MANDIRI menempati peringkat 21-30 di kelasnya.
Klasterisasi Wilayah Penghasil Tanaman Lada Menggunakan Algoritma K-Means Puspitasari, Novianti; Haviluddin, Haviluddin; Helmi Puadi, Fazma Urmila Jannah
The Indonesian Journal of Computer Science Vol. 11 No. 3 (2022): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v11i3.3104

Abstract

Wilayah potensial untuk menanam lada semakin berkurang, sehingga jumlah produksi lada menjadi semakin menurun. Hal ini tentunya perlu menjadi perhatian mengingat lada merupakan salah satu komoditas unggulan yang sangat penting untuk menunjang perekonomian. Informasi tentang daerah yang berpotensi sebagai daerah penghasil tanaman lada perlu dilakukan. Penelitian ini bertujuan untuk mendata dan menganalisa wilayah potensial untuk tanaman lada menggunakan pendekatan algoritma cerdas yaitu K-Means. Data penelitian berasal dari Dinas Perkebunan Provinsi Kalimantan Timur sebanyak 1200 data dalam rentang waktu tahun 1990 sampai 2019 telah digunakan untuk dianalisis. Lebih lanjut, ketiga metode jarak yaitu Euclidean Distance, Manhattan Distance dan Minkowski Distance digunakan dalam penelitian ini. Dari ketiga metode tersebut dicari nilai akurasi yang tertinggi menggunakan metode Silhouette Coefficient (SC). Metode Sum Square Error (SSE) dan R-squared (R2) juga digunakan untuk mengukur cluster optimal. Hasil percobaan memperlihatkan bahwa metode jarak Manhattan Distance memiliki nilai akurasi terbaik. Sedangkan, cluster optimal untuk klusterisasi wilayah diperoleh tiga cluster yang merupakan cluster ideal untuk mengelompokkan wilayah penanam lada dengan SSE sebesar 238.7377116 dan nilai R2 adalah 0.459398609. Berdasarkan hasil tersebut, diperoleh informasi tentang wilayah yang berpotensi untuk produksi lada menggunakan tiga kategori yaitu kurang berpotensi, cukup berpotensi dan berpotensi baik dengan algoritma K-Means dan metode jarak Manhattan Distance.
Application of the Apriori Algorithm to Purchase Patterns Fey, Ferry Putrawansyah
The Indonesian Journal of Computer Science Vol. 12 No. 2 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i2.3105

Abstract

The purpose of this research is to produce an Apriori Algorithm application system to increase sales turnover at Viona stores. The problem faced by the Viona store is that the Viona store has decreased turnover in the midst of business competition because it has not been able to optimally analyze the products that are often purchased and the combination of purchases by consumers so that sales seem monotonous and do not have a business strategy to attract customers. sales that can attract consumers. One way is to make sales with sales packages at lower prices. It must have a good pattern and analysis to be able to combine products into a sales package. However, with the limited ability of Information Technology, in this study a sales application was built that applies the a priori algorithm. This a priori algorithm is very effective in finding the relationship pattern of one or more itemsets in a large data set so that it is effective in calculating a sales transaction data and finding patterns of combinations of consumer habits and being able to quickly create product sales packages. increase sales turnover. The results of the process of applying the a priori algorithm to sales data at the Viona Store through the RapidMiner application are the same as the results applied to the system built and using sales transaction data for the month of May 2022 using a minimum support of 30% and minimum confidence of 30%. So from this study, information was obtained that the items that were often purchased together during this May period were lighters and cigarettes with 100% Confidence. And for the month of June Viona Stores can recommend packages in their store by looking at the results of a combination of 3 items, which are later expected to increase sales turnover at Viona Stores.
Edge Computing-Based Automated Vehicle Classification System Using the MobileNet V2 Model Widyatra Sudibyo, Rahardhita; Mahmudah, Haniah; Hadi , Moch. Zen Samsono; Sa'adah, Nihayatus
The Indonesian Journal of Computer Science Vol. 11 No. 3 (2022): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v11i3.3106

Abstract

The volume of traffic in one day is referred to as the average daily traffic volume. The Average Daily Traffic System (LHR) is also used to detect road damage caused by excessive vehicle loads. In the LHR system, vehicle data is still collected manually, with humans calculating the type and number of vehicles based on observations made and then divided into a time span. As a result, a system with a camera and deep learning data processing is required to automatically calculate the type and number of vehicles. The goal of this research is to develop edge computing systems by improving the system's performance in the calculation and classification of vehicles using the SSD MobileNet V2 model. The results of the MobileNet model scenario 5 have the lowest loss value of the five scenarios. The MobileNet V2 model can better classify vehicle types with a 65 FPS inference process.
Implementasi Learning Vector Quantization untuk Klasifikasi Jenis Buah Kelapa menggunakan Image Processing Puspita, Desi
The Indonesian Journal of Computer Science Vol. 11 No. 3 (2022): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v11i3.3108

Abstract

Coconut fruit is a versatile plant because all parts from the stem to the coconut fruit have their benefits. Coconut fruit is the most valuable part of the economy. The problem so far that has occurred is that the process of classifying coconut species is still done manually and has not been computerized, namely the classification of coconut types is still based on experience, color, and shape of the coconut. This of course takes a long time and errors still occur frequently. So this research can help classify coconuts with Learning Vector Quantization (LVQ). The purpose of this research is to organize the types of coconuts with image processing and Learning Vector Quantization (LVQ) by using mean extraction from RGB (Red, Green, Blue) and standard deviation from RGB (Red, Green, Blue). The results of the study were taken from 2 different types of coconuts against the 80 training data, the accuracy of the training data was 83.75%. The evaluation results with the Confusion Matrix with a test accuracy value of 90% of the 20 test data.
Library Research: An Online Learning at High School as Learning Media Monica Fransisca; Renny Permata Saputri; Yuliawati Yunus
The Indonesian Journal of Computer Science Vol. 11 No. 3 (2022): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v11i3.3110

Abstract

Technology has begun to applied in the field of education, not only in universities but also in high school. The existence of technology for education encourages the development of learning media, especially online learning. The purpose of this research was to examine implementation of online learning at high school level. The method used is literature review research, which is the research results come from several sources. The data obtained comes from books, research, and articles related to online learning media. Based on the research’s results, it was concluded that the use of online learning media in high school can be applied appropriately and well if it meets certain criteria. The use of online learning as learning media can also directly improved student’s learning outcomes. This statement obtained from research articles related to the topic.
Attention-based CNN-BiGRU for Bengali Music Emotion Classification Ghosh, Subhasish; Riad , Md. Omar Faruk
The Indonesian Journal of Computer Science Vol. 11 No. 3 (2022): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v11i3.3111

Abstract

For Bengali music emotion classification, deep learning models, particularly CNN and RNN are frequently used. To extract meaningful knowledge, however, past studies' shortcomings of low accuracy and overfitting have to be addressed. We have proposed a model combining Conv1D, Bi-GRU and the Bahdanau attention mechanism for music emotion classification of our Bengali music dataset. The model integrates distinct MFCCs wav preprocessing methods with deep learning methods and attention-based methods. The attention mechanism has increased the accuracy of the proposed classification model. The music is finally classified into one of the four emotion classes: Angry, Happy, Relax, Sad. The proposed Conv1D+BiGRU+Attention model is validated as more effective and efficient at classifying emotions in the Bengali music dataset than baseline methods, according to comparisons with baseline models. For our Bengali music dataset, the performance of our proposed model is 95%.
Implementasi Data Mining Pada Sistem Persediaan Obat Di Puskesmas Sei Berombang Dengan Metode Algoritma Apriori Dewi, Dewi Putri Ayu Andira; Ikhwan, Ali
The Indonesian Journal of Computer Science Vol. 11 No. 3 (2022): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v11i3.3112

Abstract

Puskesmas merupakan salah satu unit bantuan kesehatan pertama di Indonesia dan puskesmas merupakan tempat pertama untuk merawat kesehatan masyarakat dengan memberikan pelayanan dan pemberian obat sesuai dengan penyakitnya. Penyakit yang diderita langsung ditangani oleh paramedis dengan memberikan obat-obatan. Pentingnya persediaan obat di puskesmas akan menjadi prioritas terpenting yang harus distok untuk menghindari dan mengantisipasi kelangkaan obat. Strategi yang dapat dilakukan untuk mengatasi permasalahan stok obat di puskesmas adalah dengan mengikuti pola penggunaan obat dengan menerapkan data mining di puskesmas dalam memprediksi pola penggunaan obat dengan metode yang tepat menggunakan algoritma apriori yaitu bertujuan untuk mengetahui adanya keterkaitan asosiatif antara kombinasi produk satu dengan lainnya disebut dengan metode yang menerapkan aturan asosiasi. Sehingga puskesmas mengetahui obat yang paling banyak digunakan dan dapat memilah persediaan obat yang harus dipenuhi.
Sistem Keamanan Rumah Menggunakan Sensor Passive Infrared Receiver dan SMS Gateway Berbasis Arduino Prasetio, Yoga Randi; Satria, Budy
The Indonesian Journal of Computer Science Vol. 11 No. 3 (2022): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v11i3.3113

Abstract

Weak security systems in a house provide opportunities and opportunities for other people who are not entitled to take and steal valuables for homeowners. Based on these problems, research was carried out by making a home security system using a passive infrared receiver sensor and an Arduino-based sms gateway. The system is designed using electronic devices such as Arduino Microcontroller as data processing, PIR sensor to detect movement in the house, buzzer as an alarm and GSM 900 A module as a communication medium in the form of SMS which is connected to the homeowner's cell phone. This system works when the PIR sensor (Passive InfraRed Receiver) detects a human entering the house and then Arduino sends data in the form of an alarm from the buzzer and sends a danger sign in the form of SMS (Short Message Service). The results of the research conducted on the PIR sensor with a distance of 30-150 cm on the object movement caused an alarm to sound originating from the buzzer and SMS notifications were also successfully sent to the homeowner every time there was movement or an open door detected by the PIR sensor. This research shows that technology can be a solution to prevent crime, especially at home through a home security system using PIR sensors and SMS Gateway
Penerapan Algoritma MOORA dalam Menentukan Sekolah Dasar Terbaik Athif Fauzan; Supriatin
The Indonesian Journal of Computer Science Vol. 11 No. 3 (2022): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v11i3.3114

Abstract

Elementary school is the first level of education for students who have an important role in the development of behavior, skills and talents. Thus, the selection of the best primary school becomes something important for parents, because this will affect the future of their children in the future. In this regard, parents are faced with many choices of primary schools, especially in the Purworejo area for their children. This study aims to assist parents in determining the best primary school that is right for their children by using a Decision Support System. The method used in this research is MOORA. Multi Objective Optimization On The Basic Of Ratio Analysis (MOORA) is a method that can filter the best alternatives because this method is able to determine goals based on conflicting criteria on several constraints, so the use of this method is very appropriate to solve existing problems. This study uses 6 criteria that have been determined as a form of reference in determining the best elementary school, namely Accreditation, School Location, School Facilities, School Achievement, Professional HR (Teachers), and Number of Excellent Programs. There are 7 elementary schools that will be used as alternative data, all of which are taken from elementary schools in Purworejo. Of the 7 elementary schools taken, 3 elementary schools with school codes SK03 = 0.3382 , SK01 = 0.2922 , and SK02 = 0.2538 were selected to be the best elementary schools in 2022.

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