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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Pusat Inovasi Masyarakat
Gunawan, Windi
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Computer Science & IT Engineering Social Sciences

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Strategi Pengembangan Budi Daya dan Pemasaran Ubi Jalar di Desa Sukajadi, Kecamatan Tamansari, Kabupaten Bogor Kartika, Lindawati; Gunawan, Windi; Lubis, Lutfi Syahreza; Jouhary, Naufal Amir; Atsemani, Jezila Amana Nousra; Saputro, Ilham Tyas Sulistyo; Shinta, Karennina; Sukardi, Muhammad Mirza; Cantika, Silvi
Jurnal Pusat Inovasi Masyarakat Vol. 6 No. 2 (2024): Oktober 2024
Publisher : Direktorat Pengembangan Masyarakat Agromaritim, Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jpim.6.2.208-220

Abstract

Sukajadi Village, located in Kecamatan Tamansari, Kabupaten Bogor, has significant agricultural land with the majority of its residents being farmers. Despite its high agricultural potential, most farmers still face various challenges such as traditional marketing methods, unstable crop prices, limited commodity variety, and lack of knowledge about cultivation techniques. One of the potential yet underutilized commodities is sweet potatoes. KKN-T IPB students designed the Agriwork program, a workshop on sweet potato cultivation and marketing techniques, in collaboration with the Agribusiness and Technology Park (ATP) of IPB University. This program aims to enhance farmers' understanding of effective cultivation and marketing techniques, and establish partnerships with ATP IPB University. The implementation of the program involved four main stages: socialization, land survey, assistance, and partnership. The Agriwork program successfully initiated partnerships between Sukajadi Village farmers and ATP IPB University. Three farmers from local farmer groups expressed interest in partnering, two of whom have undergone land surveys, and one has started planting seedlings. This partnership continues with the harvesting process of 3.5 tons of white and yellow sweet potatoes, supervised by ATP IPB University, and the preparation for signing the memorandum of understanding (MOU) between the farmers and ATP IPB. The AgriWork program by KKN-T IPB and ATP IPB supports Sukajadi farmers in improving sweet potato production and marketing. Three farmers became partners, receiving production assistance, price assurance, and access to modern markets, positioning Sukajadi as a potential sweet potato hub in Bogor.
Perbandingan Performa Arimax-Garch Dan Lstm Pada Data Harga Penutupan Saham PT Aneka Tambang Tbk (ANTM.JK) Suwarso, Dhiya Khalishah Tsany; Rizki, Akbar; Rahmi, Salsabila Dwi; Mahesa, Hakim Zoelva; Gunawan, Windi; Fitri, Zafira Ilma; Angraini, Yenni; Putri, Adelia; Nurhambali, Muhammad Rizky
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 3: Juni 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025128756

Abstract

Banyaknya data deret waktu dengan pola nonlinear dan memiliki volatilitas tinggi pada berbagai sektor membuat sulit untuk melakukan pemodelan klasik seperti Autoregressive Integrated Moving Average (ARIMA). Permasalahan ini dapat diatasi salah satunya dengan mengembangkan metode Autoregressive Integrated Moving Average with Exogenous- Generalized Autoregressive Conditional Heteroskedasticity (ARIMAX-GARCH) yang memanfaatkan kovariat eksternal, sehingga memberikan solusi lebih baik pada data yang tidak stasioner. Di sisi lain, metode deep learning seperti Long Short-Term Memory (LSTM) unggul dalam menangkap pola non-linear dan dependensi jangka panjang. Oleh karena itu, penelitian ini membandingkan performa ARIMAX-GARCH dan LSTM dalam memprediksi harga saham PT Aneka Tambang Tbk (ANTM.JK). Data mingguan penutupan harga saham ANTM.JK periode 1 Januari 2018 hingga 30 Oktober 2023 digunakan dalam penelitian ini. Pemodelan ARIMAX-GARCH dengan peubah kovariat berupa data harga nikel berjangka dunia digunakan karena terdapat pengaruh signifikan harga nikel terhadap harga penutupan saham ANTM.JK dan terdeteksi adanya heteroskedastisitas dalam model. Metode berbasis machine learning, LSTM digunakan karena metode ini dikenal memiliki akurasi prediksi yang baik. Pengolahan data dilakukan menggunakan bantuan software R-Studio dan Python. Hasil penelitian menunjukkan LSTM memiliki performa yang lebih baik dengan nilai MAPE sebesar 4,425%, nilai ini lebih kecil jika dibandingkan model terbaik ARIMAX(2,1,2)-GARCH(1,1) dengan MAPE 7,326%.   Abstract The large number of time series data with nonlinear patterns and high volatility in various sectors makes it difficult to perform classical modeling such as Autoregressive Integrated Moving Average (ARIMA). This problem can be overcome by developing the ARIMA with Exogenous- Generalized Autoregressive Conditional Heteroskedasticity (ARIMAX-GARCH) that utilizes external covariates, thus providing a better solution to non-stationary data. On the other hand, deep learning methods such as Long Short-Term Memory (LSTM) excel in capturing non-linear patterns and long-term dependencies. Therefore, this study compares the performance of ARIMAX-GARCH and LSTM in predicting the stock price of PT Aneka Tambang Tbk (ANTM.JK). Weekly closing data of ANTM.JK stock price from January 1, 2018 to October 30, 2023 are used in this study. ARIMAX-GARCH modeling with covariate variables in the form of world nickel futures price data is used because there is a significant effect of nickel prices on the closing price of ANTM.JK shares and heteroscedasticity is detected in the model. Machine learning-based method, LSTM is used because this method is known to have good prediction accuracy. Data processing is done using R-Studio and Python software. The results show that LSTM has better performance with a MAPE value of 4.425%, this value is smaller than the best model ARIMAX(2,1,2)-GARCH(1,1) with a MAPE of 7.326%.
Co-Authors Atsemani, Jezila Amana Nousra Cantika, Silvi Fitri, Zafira Ilma Jouhary, Naufal Amir Lindawati Kartika Lubis, Lutfi Syahreza Mahesa, Hakim Zoelva Nurhambali, Muhammad Rizky Putri, Adelia Rahmi, Salsabila Dwi Rizki, Akbar Saputro, Ilham Tyas Sulistyo Shinta, Karennina Sukardi, Muhammad Mirza Suwarso, Dhiya Khalishah Tsany Yenni Angraini