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Darius Andana Haris
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jiksi@fti.untar.ac.id
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INDONESIA
JIKSI (Jurnal Ilmu Komputer dan Sistem Informasi)
Published by Universitas Tarumanagara
ISSN : 23028769     EISSN : 23032529     DOI : -
Core Subject : Science, Education,
Computer Science & IT Mathematics Other
Jurnal Ilmu Komputer dan Sistem Informasi (JIKSI) diterbitkan oleh Fakultas Teknologi Informasi Universitas Tarumanagara (FTI Untar) Jakarta sebagai media publikasi karya ilmiah mahasiswa program studi Teknik Informatika dan Sistem Informasi FTI Untar. Karya-karya ilmiah yang dihasilkan berupa hasil penelitian kualitatif dan kuantitatif, perancangan sistem informasi, analisis dan perancangan progam aplikasi. Jurnal ini terbit dua kali dalam setahun yaitu pada bulan Januari dan Agustus.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 937 Documents
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SISTEM INFORMASI MONITORING MURID OLEH GURU DENGAN ORANGTUA BERBASIS WEB DAN MOBILE Sudono Widjaja; Bagus Mulyawan; Novario Jaya Perdana
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 11 No. 2 (2023): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v11i2.26003

Abstract

In 2019, pandemic hits the world and the world was forced to change their way to do normal activities, including schools to do school activities from home. Elementary School children don’t yet know how to use devices properly therefore their parents have to help and guide them for them to understand the tasks given by school. The Information system for monitoring students expected able to help teachers and parents to coordinate and evaluate their student’s data. This system is made using two platforms, namely web-based whose main feature is for teacher to enter the students data, and mobile-based to view data that has been enetered from the web.
KLASIFIKASI HASIL BELAJAR SISWA MENGGUNAKAN METODE C4.5 BERDASARKAN RIWAYAT AKADEMIK DI SMP XYZ Bryan Daniel Pinenda Pasaribu; Tri Sutrisno; Bagus Mulyawan
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 11 No. 2 (2023): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v11i2.26004

Abstract

This research was conducted to classify student learning outcomes at XYZ Middle School based on academic history during learning and student learning interests. This study aims to provide information to students and teaching staff regarding student learning outcomes. With the available information, it is hoped that teaching staff can develop methods for conveying material in order to obtain better results. The method used in solving this problem is the C.45 algorithm method. Starting from collecting data consisting of assignment scores, daily tests, UTS, and UAS. Then the formation of a decision system as initial data that has condition and decision attribute values. Then calculate the entropy value of each attribute. Calculating the highest gain value which will then be used as a node. Then, determine the decision from the results of the decision tree process by starting from the highest root to the lowest root to determine the decision criteria.
Aplikasi Monitoring Tunggakan Uang Kuliah Mahasiswa Non Aktif Di Universitas Tarumanagara Menggunakan Metode Naive Bayes Timothy Reynaldi; Lely Hiryanto; Darius Andana Haris
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 11 No. 2 (2023): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v11i2.26005

Abstract

Universitas Tarumanagara memiliki dua status mahsiswa, yaitu mahasiswa aktif dan mahasiswa non aktif. Saat ini, bidang administrasi di Universitas Tarumanagara belum memiliki sistem yang baik untuk menangani tunggakan uang kuliah dari mahasiswa non aktif. Tujuan dari perancangan Aplikasi Monitoring Tunggakan Uang Kuliah Mahasiswa Non Aktif ini adalah untuk memperbaiki dan memudahkan user untuk memonitoring tunggakan uang kuliah dari mahasiswa non aktif di Universitas Tarumanagara. Aplikasi ini menggunakan metode Naive Bayes. Penerapan dari metode Naive Bayes ini berfungsi untuk menghitung probabilitas kemungkinan mahasiswa Universitas Tarumanaga yang non aktif selama tiga semester berturut-turut harus di keluarkan atau tidak. Hasil dari penerapan metode Naive Bayes ini berhasil untuk menampilkan output prediksi untuk dikeluarkan atau dilanjutkannya mahasiswa yang sudah non aktif selama tiga semester berturut-turut. Hasil dari pengujian fungsional aplikasi menggunakan mendapatkan output sukses untuk pengetesan pada semua halaman yang di uji dan metode pengambilan keputusan dari aplikasi ini memiliki akurasi untuk prediksi tindakan pengambilan keputusan sebesar 91%.
PERBANDINGAN KNN DAN SVM UNTUK KLASIFIKASI KUALITAS UDARA DI JAKARTA Bryan Valentino Jayadi; Teny Handhayani; Manatap Dolok Lauro
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 11 No. 2 (2023): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v11i2.26006

Abstract

The growth and economic development of a city is one of the factors causing air pollution because air quality has been mixed with various components of chemical compounds such as motor vehicle exhaust gases and factory smoke waste. Data mining is a method to find out information about air pollution in the city of Jakarta. The data mining method used is classification because this method can process air pollution standard index (AQI) parameter data into information that can show the level of air quality per day using the K-Nearest Neighbor algorithm and Support Vector Machine. The result of the application of data mining for air quality classification in Jakarta is that the Support Vector Machine algorithm has better accuracy performance compared to the K-Nearest Neighbor algorithm. The Support Vector Machine algorithm uses the RBF kernel and 100 kernel parameter gets an accuracy value of 98%, precission of 97%, recall of 97%, and F1-Score of 97% while the K-Nearest Neighbor algorithm uses the number of K as much as 6 gets an accuracy value of 96%, precission of 96%, recall of 93%, and F1-Score of 94%.
IMPLEMENTASI AES UNTUK KEAMANAN APLIKASI FORMULIR ONLINE Andri Firnandius; Lely Hiryanto
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 11 No. 2 (2023): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v11i2.26011

Abstract

Google Forms software is an online application which users can create form for various purpose. The application can store information or data that has been provided by the form fillers. The form fillers are merely identified by their institutional email's domain or those with the access link to the make response for each question provided in an online form. The use of third-party applications certainly reduces the sense of trust in the security of the data provided. Therefore, a digital form application design was created with the Advanced Encryption Standard (AES). The aim is to maintain the security of the data provided by the form filler and ensure that the fillers are those with the authority.
PREDIKSI HARGA PANGAN DI PASAR TRADISIONAL KOTA SURABAYA DENGAN METODE LSTM Teddy Ericko; Manatap Dolok Lauro; Teny Handhayani
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 11 No. 2 (2023): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v11i2.26012

Abstract

Long Short-Term Memory is the development of an artificial neural network that has the ability to overcome the vanishing gradient problem, and makes it possible to remember long-term information, and understand temporal patterns in time series data, so that LSTM has good performance in predicting food prices [1]. In Indonesia, especially in Surabaya, food prices are often unstable. Fluctuations in food prices can be caused by many factors such as weather, growing season and production. Under these conditions, this research was conducted to predict future food prices. The purpose of this study is to apply the LSTM method in predicting food prices so that it can provide maximum results and can be used by the community in making good decisions. In this study the dataset used included 5 types of food, namely rice, beef, chicken eggs, granulated sugar, and cooking oil. The dataset was obtained from the website of the National Strategic Food Price Information Center (PIHPS Nasional, https://www.bi.go.id/hargapangan). Predictive results are evaluated with RMSE and MAE. RMSE and MAE values of 5 types of food, namely rice 32 and 27, beef 229 and 125, chicken eggs 319 and 213, cooking oil 424 and 215, granulated sugar 30 and 18.
PREDIKSI HARGA PANGAN KOTA BANDUNG MENGGUNAKAN METODE GATED RECURRENT UNIT Matthew Oni; Manatap Dolok Lauro; Teny Handhayani
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 11 No. 2 (2023): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v11i2.26014

Abstract

Food problems often occur among the community, this occurs due to a lack of predictions made to determine future food prices. Food prices can be achieved if the government can provide sufficient food supplies both in terms of quality and quantity. The availability of sufficient food is an important factor in maintaining the health and welfare of the community. However, the high price fluctuations of staple foods in traditional markets have a negative impact on the availability and quality of food for the community, especially those with low incomes. This was caused by various factors such as rising raw material prices, the influence of weather factors, and changes in people's consumption patterns. In addition, the process of distribution and marketing of staple foods in traditional markets in Bandung City, which still relies on manual processes and is less structured, can also cause high price fluctuations. Therefore we need an application to predict staple food needs for the future accurately and effectively. This study uses the Gated Recurrent Unit method. This method is used because the Gated Recurrent Unit method has good performance in making predictions and fits the data used for this study. In this study, there were 5 types of commodities used, namely rice, chicken meat, chicken eggs, shallots, and garlic. All datasets used were taken from the website of the National Strategic Food Price Information (PIHPSNasional, https://www.bi.go.id/hargapangan). Predictive results by evaluating MAE and MAPE for rice 12.8, and 0.10, for chicken meat 12.8 , and 0.10, for chicken egg 244.5, and 0.64, for onion 296.9, and 1.05, for garlic 602.8, and 1.32.
PERBANDINGAN LSTM DAN ELM DALAM MEMPREDIKSI HARGA PANGAN KOTA TASIKMALAYA Andry Winata; Manatap Dolok Lauro; Teny Handhayani
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 11 No. 2 (2023): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v11i2.26015

Abstract

Humans have needs that must be met, one of which is the need for food, but food prices often change. Factors that affect price changes occur because the amount of demand is high while the supply is small. Making predictions about price changes will be very helpful to get an idea of the pattern of price changes. Therefore making predictions from price patterns is useful for providing information to the public. Predictions regarding price changes can be made using many methods. Long Short-Term Memory (LSTM) and Extreme Learning Machine (ELM) are several methods that can be used to predict time series data, these two methods can provide an overview of the predictions made. The results of the study show that both algorithms have good results in terms of the the evaluation value. The evaluation results showed no significant difference between the two algorithms. The evaluation value of the rice commodity showed that ELM tended to be better with MAE values of 6,721, MAPE 0.061%, MSE 115,281, RMSE 10,737 and CV 3,699%, while LSTM with MAE 31,707, MAPE 0.286%, MSE 1927.633, RMSE 43.905 and CV 3.655%. However, for other commodities, LSTM can produce a better evaluation value.
PERANCANGAN SISTEM PENDUKUNG KEPUTUSAN UNTUK MEMUDAHKAN PEMILIHAN KEDAI KOPI Calvin; Hugeng; Tri Sutrisno
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 11 No. 2 (2023): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v11i2.26016

Abstract

A decision support system (DSS) for selecting coffee shops is a technological solution that can help coffee enthusiasts find coffee shops that suit their preferences. The purpose of this research is to design a decision support system to facilitate the selection of coffee shops. The methodology used in this research is the waterfall method which consists of needs analysis, design, implementation, and system testing. Needs analysis is carried out by conducting surveys and interviews with respondents who are coffee consumers. The results of the needs analysis will be used as the basis for system design. The system design is carried out using the ERD and DFD models, as well as the selection of the right programming language and database. Implementation is done by developing the system according to the design that has been made. Finally, system testing is carried out to ensure that the system can run properly and according to user requirements. It is hoped that the results of this study can provide benefits for coffee fans in choosing coffee shops that suit their preferences.
SISTEM INFORMASI PRODUKSI PADA PT. WANAPOTENSI NUSA BERBASIS WEB Ricky Giovanni Putra Tanjaya; Dedi Trisnawarman
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 11 No. 2 (2023): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v11i2.26017

Abstract

PT Wanapotensi Nusa is a company engaged in the processing of logs. Recording of production carried out by PT Wanapoten Nusa when this journal was made was still using the manual method, making it difficult for the company to analyze and also make decisions on production data. Against the background of these problems, a production information system was proposed to be able to store and display data. production so that it is expected to help companies to be able to facilitate access to production data. To make it easier for companies to make decisions about production data in this journal, a production recommendation method will also be discussed using the Economic Production Quantity (EPQ) which is expected to provide input in the form of production suggestions for the next month.

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