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All Journal JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING BERKAT: Jurnal Pemberdayaan Masyarakat
Adam, Hikmah Adwin
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Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Engineering Environmental Science Social Sciences

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Comparison of ARIMA and LSTM Models in Stock Price Forecasting: A Case Study of GOTO.JK Adam, Hikmah Adwin; Raditiansyah, Farhan; Imani, Muhammad Rayyan; Fawwaz, Mohammad Faris; Julham, Julham; Lubis, Arif Ridho
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 1 (2024): Issues July 2024
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i1.11841

Abstract

The renowned Indonesian company, PT Gojek Tokopedia Tbk, has a significant impact on the Indonesian economy by attracting investors to invest their shares. This study uses stock closing price data to forecast stock prices using ARIMA (AutoRegressive Intergrated Moving Average) and LSTM (Long Short-Term Memory) models, to predict using prediction by dividing the data into groups of 10 or 20 data with data sets to be trained as multiples. The analysis shows that ARIMA is superior to LSTM based on the comparison of average error and average percentage error, where the average error results in LSTM (3.843) and ARIMA (3.259), as well as the average error of LSTM (4.04%) and ARIMA (3.57%). The research supports the conclusion that ARIMA has a better performance in predicting the stock price of PT Gojek Tokopedia Tbk. These results provide important insights for investors and market participants, while the research supports the increased use of seasonal patterns in ARIMA forecasting for more accurate results in the future. Future research is recommended to explore additional factors and optimized models to further improve stock price prediction.
Machine Learning-Driven Detection of Malicious URL: Comparative Analysis of Random Forest and SVMs Adam, Hikmah Adwin; Nasution, Shaquil Fathza; Simanungkalit, Rikky Rifaldo; Diansyah, Ikhsan Hafid
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 1 (2024): Issues July 2024
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i1.11844

Abstract

No longer a novelty, the internet has become the ubiquitous fabric of our lives, transforming how we interact, do business and disseminate information. However, its popularity has also attracted attackers who want to exploit it for personal gain. One tactic they use is to launch client-side attacks through malicious websites. Malicious websites are constantly evolving, and traditional methods such as blacklisting are no longer effective in identifying them. More sophisticated and adaptive solutions are needed to combat this threat. This research proposes an automatic malicious website detection method that utilizes URL properties and machine learning algorithms. This approach uses a combination of relevant URL features and a powerful machine learning model to accurately identify malicious websites. This research uses two popular machine learning algorithms: Random Forest (RF) and Support Vector Machines (SVM). Both models are trained on a dataset consisting of URL properties of malicious and Benign websites. The research results show that the proposed method is able to achieve a good level of accuracy in detecting malicious websites. Both RF and SVM show promising performance, with RF model achieved an accuracy of 86.15%, surpassing the SVM's performance of 85.38%. While overall performance is satisfactory, further optimization might be necessary, particularly to address potential class imbalance. Oversampling method could offer a more effective alternative to traditional undersampling methods and potentially improve performance across both website URLs categories
Peningkatan Kapasitas Serapan Pakan Hijauan Untuk Mempercepat Penggemukan Lembu Dengan Memanfaatkan Mesin Pencacah Rumput Di Desa Bangun Rejo Kabupaten Deli Serdang Suadi; Suprapto; Zumhari; Adam, Hikmah Adwin
BERKAT: Jurnal Pemberdayaan Masyarakat Vol 3 No 2 (2023): DESEMBER 2023
Publisher : P3M Politeknik Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pengabdian Kemitraan Masyarakat (PKM) di Desa Bangun Rejo, Kecamatan Tanjung Morawa, khususnya pada keluarga mitra Ibu Sismawati, bertujuan untuk membantu mengatasi masalah usaha peternakan lembu. Mitra memiliki 15 ekor lembu yang diberi pakan hijauan berupa rumput. Sebagian lembu dewasa digembala di ladang, sementara yang memerlukan perawatan intensif diransum di kandang. Pemberian rumput dari lahan tegalan warga, yang dicacah dengan parang, kadang dilakukan tidak sempurna karena kelelahan. Sebagai dampaknya, sekitar 20% rumput tidak dimakan oleh lembu, terutama bagian pangkal. Hal ini mengakibatkan serapan hijauan lembu hanya mencapai 80%, menyebabkan keterlambatan pertumbuhan dan penggemukan lembu. Dalam pola pemberian ransum ini, mitra harus menunggu 24-26 bulan agar lembu mencapai bobot layak jual, yang berdampak pada meningkatnya biaya perawatan. Untuk mengurangi biaya perawatan, perlu mempercepat penambahan bobot lembu dengan meningkatkan serapan pakan hijauan mendekati 100%. Tim pelaksana PKM membantu mitra dengan teknologi tepat guna, yaitu alat pencacah rumput. Langkah-langkah kegiatan melibatkan identifikasi masalah, perancangan solusi alternatif, pemilihan solusi tepat, realisasi solusi, dan penerapan pada masalah mitra. Harapan program ini adalah dapat mengurangi biaya produksi, meningkatkan kesejahteraan keluarga mitra, dan memastikan tidak ada pakan hijauan yang terbuang.
Co-Authors Diansyah, Ikhsan Hafid Fawwaz, Mohammad Faris Imani, Muhammad Rayyan Julham Julham Lubis, Arif Ridho Nasution, Ali Napiah Nasution, Shaquil Fathza Raditiansyah, Farhan Simanungkalit, Rikky Rifaldo SUPRAPTO Zumhari