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All Journal JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JTIK (Jurnal Teknik Informatika Kaputama) BERKAT: Jurnal Pemberdayaan Masyarakat Journal of Computer Science Advancements
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

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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.
PERANCANGAN DAN PEMBUATAN TRAINER KOMUNIKASI RS232 MENGGUNAKAN KOMPUTER DAN MIKROKONTROLER ATMEGA Julham, Julham; Adam, Hikmah Adwin
JTIK (Jurnal Teknik Informatika Kaputama) Vol. 2 No. 1 (2018): Volume 2, Nomor 1, Januari 2018
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jtik.v2i1.663

Abstract

Salah satu sistem komunikasi antara komputer dengan mikrokontroler adalah komunikasi serial jenis RS232. Komunikasi jenis ini dibahas dalam matakuliah Interfacing pada program studi Teknik Komputer Jurusan Teknik Komputer dan Informatika Politeknik Negeri Medan. Selama ini proses belajar mengajarnya hanya menggunakan aplikasi simulasi yang dipasang di komputer. Kelemahan dengan simulasi ini adalah peserta didik tidak mengetahui proses yang sebenarnya terjadi, mulai dari persiapan perangkat keras yang diperlukan, perakitan dan penggunaanya. Berangkat dari kendala tersebut peneliti membuat modul pembelajaran berupa trainer dan jobsheetnya yang terdiri dari dua percobaan dasar.Metode yang digunakan dalam penelitian ini adalah Research and Development.Metode ini menggunakan tahapan yang telah diringkas kemudian dikelompokkan menjadi 3 fase besar sehingga variabel, model, dan hasil tiap tahapan diketahui 3 fase besar.Setelah trainer beserta modul tersebut dibuat maka diujicobakan ke peserta didik untuk mengetahui umpan baliknya.Umpan balik yang diberikan dalam bentuk kuesioner. Setelah itu hasil kuesioner diolah dan didapatlah total rata-rata hasil rating adalah 70,10%. Dan menurut skala likert nilai tersebut tergolong layak
The Application of Artificial Intelligence in Processing Health Data in Biomedical Information Prayudani, Santi; Lase, Yuyun Yusnida; Husna, Meryatul; Adam, Hikmah Adwin
Journal of Computer Science Advancements Vol. 3 No. 2 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i2.2245

Abstract

The increasing complexity and volume of health data in modern biomedical systems have necessitated advanced technologies for effective data processing and analysis. Traditional methods often fall short in managing real-time, multidimensional data generated from various biomedical sources, such as electronic health records (EHRs), wearable devices, and genomic data. This research investigates the application of artificial intelligence (AI) in optimizing the processing and interpretation of biomedical health data. The objective of this study is to explore how AI-based technologies, including machine learning and deep learning algorithms, enhance the efficiency, accuracy, and predictive capabilities in biomedical information systems. By identifying patterns, anomalies, and correlations in large datasets, AI offers potential improvements in disease diagnosis, patient monitoring, and treatment personalization. This research employs a qualitative systematic review method, analyzing peer-reviewed literature published between 2015 and 2024 from major databases such as PubMed, IEEE Xplore, and Scopus. The analysis focuses on case studies, comparative evaluations, and implementation outcomes of AI in various biomedical domains. The findings reveal that AI applications significantly improve data processing speed and accuracy, enable early diagnosis of diseases such as cancer and diabetes, and support predictive analytics for patient outcomes. However, challenges remain in areas such as data privacy, ethical compliance, and algorithm transparency. In conclusion, the integration of AI into biomedical data systems holds transformative potential for healthcare delivery, though further interdisciplinary collaboration is required to address its limitations and ensure equitable access and ethical use.
Co-Authors Diansyah, Ikhsan Hafid Fawwaz, Mohammad Faris Husna, Meryatul Imani, Muhammad Rayyan Julham Julham Lubis, Arif Ridho Nasution, Ali Napiah Nasution, Shaquil Fathza Prayudani, Santi Raditiansyah, Farhan Simanungkalit, Rikky Rifaldo SUPRAPTO Yuyun Yusnida Lase Zumhari