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All Journal Jurnal Manajemen Informatika
R. A. E. Virgana Targa Sapanji
Universitas Widyatama
Computer Science & IT

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Perancangan Desain Sistem Informasi Geografis Pemetaan Desa Mandiri Energi Kec. Pangalengan Kab. Bandung R. A. E. Virgana Targa Sapanji; Dani Hamdani
Jurnal Manajemen Informatika (JAMIKA) Vol 10 No 1 (2020): Jurnal Manajemen Informatika (JAMIKA)
Publisher : Program Studi Manajemen Informatika, Fakultas Teknik dan Ilmu Komputer, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (4379.922 KB) | DOI: 10.34010/jamika.v10i1.2571

Abstract

Pangalengan District has its uniqueness and uniqueness, such as the height factor, agricultural groups, livestock groups, community plantations or large / state plantations. The problem is that there is no map of the distribution of energy sources in Pangalengan Subdistrict, as many cattle farms almost all have biogas reactors, as well as smallholder plantations that are scattered in almost every village where there is biomass energy potential, lakes such as Cileunca where the water flow is used by around 3 state electricity companies (Indonesia Power) reached the Cikalong area, and there are hundreds of hectares of large / state plantations operating in Pangalengan District, but whether the yield of plantation biomass has been optimized for energy purposes. The method used is geoprocessing to overlay several maps and data so that the data conclusions drawn information, the results of this study in the form of a distribution map of potential biological resources that can be used as an energy source, and the expected impact hopefully the community and government officials in Pangelang District better understand the potential for independent energy in the area.
Prediksi Indeks Bursa Efek Indonesia 2023 Pendekatan ARIMA, Machine Learning dengan R Programming R. A. E. Virgana Targa Sapanji; Sri Lestari; Murnawan Murnawan; Rosalin Samiharjo
Jurnal Manajemen Informatika JAMIKA Vol 13 No 2 (2023): Jurnal Manajemen Informatika (JAMIKA)
Publisher : Program Studi Manajemen Informatika, Fakultas Teknik dan Ilmu Komputer, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/jamika.v13i2.10777

Abstract

The Indonesian Stock Exchange Index (BEI) is the main indicator of the Indonesian stock market. The problem in this research is a specific issue to solve the practical problem of predicting the future movement of the BEI index which has strategic value for investors, traders and companies in Indonesia. The aim of this research is to be able to predict the BEI index until the end of 2023, because the stock exchange plays a big role for the Indonesian economy as an economic and financial function. Traditional approaches such as ARIMA (Autoregressive Integrated Moving Average) have been used to predict the movement of the BEI index. However, in recent years, machine learning and data mining techniques have become popular as more sophisticated alternative approaches. This research uses a combined approach between ARIMA and machine learning with R Programming. Daily IDX index data from January 2012 to December 2022 will be taken from Yahoo Finance. This data will then be cleaned and processed using R Programming. The ARIMA approach will be used as a baseline to compare machine learning performance. The results focus on the estimated closing stock prices for the next 365 days or the average until the end of 2023. The Time Series value of the possible minimum/maximum predicted value for IDX shares in 2023 is a minimum predicted value of 6786,212 - 6849,559, a maximum predicted value of 6850,093 - 7086,012. Trends represent a good approach in predicting the future direction of closing prices.
Co-Authors Dani Hamdani Murnawan, Murnawan Rosalin Samiharjo Sri Lestari