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All Journal Gaung Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Go Infotech: Jurnal Ilmiah STMIK AUB Jurnal Tekinkom (Teknik Informasi dan Komputer) TEPIAN Jurnal Pengabdian Teknologi Tepat Guna SPEED - Sentra Penelitian Engineering dan Edukasi Amarasi : Jurnal Desain Komunikasi Visual Jurnal Sains dan Teknologi JOMLAI: Journal of Machine Learning and Artificial Intelligence Engineering and Technology International Journal (EATIJ) Jurnal Pengabdian Masyarakat dan Riset Pendidikan Go Infotech: Jurnal Ilmiah STMIK AUB Jurnal Nasional Teknologi Informasi dan Aplikasinya
Ruswanti, Diyah
Universitas Sahid Surakarta
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Implementasi Metode LSTM untuk Prediksi Harga Saham PT Indofood CBP Sukses Makmur TBK Endritha Pramudya; Dwi Retnoningsih; Diyah Ruswanti
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 4 (2025): JNATIA Vol. 3, No. 4, Agustus 2025
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2025.v03.i04.p24

Abstract

High stock price fluctuations make stock prices difficult to predict accurately. Therefore, a predictive analysis approach that utilizes historical data in addition to machine learning methods is needed to help estimate price movements more effectively. This study aims to determine the performance of the Long Short-Term Memory (LSTM) method in predicting the stock prices of PT Indofood CBP Sukses Makmur Tbk based on historical data. LSTM is a type of artificial neural network that is effective in processing time series data due to its ability to capture long term relationships between data. Historical data is used to train the LSTM model. The results show that the LSTM model is effective in predicting stock prices, with an average accuracy of 80.5%. Sukses Makmur Tbk based on historical data. LSTM is one type of artificial neural network that is effective in processing time series data due to its ability to capture long-term relationships between data points. The data used consists of ICBP stock closing prices from January 2019 to May 2025. The methods used include data cleaning, data normalization, data splitting, model design, prediction, denormalization, and evaluation using the Mean Absolute Percentage Error (MAPE) metric. The research results demonstrate that the LSTM model performs well in recognizing time series data patterns, as indicated by the lowest MAPE value of 1.43, at the combination of 100 epochs and a batch size of 32.
Rancang Bangun IoT Monitoring Kelembapan Tanah dan Penanaman Jagung Otomatis Purnama, Christian Roy; Charolina, Astri; Khusnuliawati, Hardika; Ruswanti, Diyah
Go Infotech: Jurnal Ilmiah STMIK AUB Vol 31, No 2 (2025): December
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer AUB - Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36309/goi.v31i2.417

Abstract

Petani jagung di Desa Balong, Kabupaten Wonogiri, menghadapi permasalahan dalam proses penanaman biji jagung yang masih bersifat konvensional yang membuat biaya penananam tinggi, serta kurangnya informasi tentang kondisi kelembapan tanah yang berpotensi menyebabkan gagal panen. Penelitian ini bertujuan untuk merancang dan membangun alat penanaman jagung secara otomatis dan alat untuk memantau kelembapan tanah secara real-time berbasis Internet of Things (IoT).Pembangunan alat menggunakan mikrokontroler ESP32, sensor kelembapan tanah, servo motor, LCD, dan tombol sebagai input. Memanfaatkanaplikasi BLYNK untuk monitoring kelembaban tanah, Telegram untuk notifikasi ke petani, dan Google Spreadsheet untuk pencatatan data. Penelitian ini menggunakan metode prototyping dengan pendekatan studi literatur, observasi, dan wawancara kepada petani lokal. Hasil pengujian menunjukkan bahwa alat dapat berfungsi dengan baik pada lahan seluas 6x5 meter, memberikan informasi kelembapan tanah secara akurat dan realtime dapat diakses melalui telegram.
Rancang Bangun IoT Monitoring Kelembapan Tanah dan Penanaman Jagung Otomatis Purnama, Christian Roy; Charolina, Astri; Khusnuliawati, Hardika; Ruswanti, Diyah
Go Infotech: Jurnal Ilmiah STMIK AUB Vol 31, No 2 (2025): December
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer AUB - Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36309/goi.v31i2.417

Abstract

Petani jagung di Desa Balong, Kabupaten Wonogiri, menghadapi permasalahan dalam proses penanaman biji jagung yang masih bersifat konvensional yang membuat biaya penananam tinggi, serta kurangnya informasi tentang kondisi kelembapan tanah yang berpotensi menyebabkan gagal panen. Penelitian ini bertujuan untuk merancang dan membangun alat penanaman jagung secara otomatis dan alat untuk memantau kelembapan tanah secara real-time berbasis Internet of Things (IoT).Pembangunan alat menggunakan mikrokontroler ESP32, sensor kelembapan tanah, servo motor, LCD, dan tombol sebagai input. Memanfaatkanaplikasi BLYNK untuk monitoring kelembaban tanah, Telegram untuk notifikasi ke petani, dan Google Spreadsheet untuk pencatatan data. Penelitian ini menggunakan metode prototyping dengan pendekatan studi literatur, observasi, dan wawancara kepada petani lokal. Hasil pengujian menunjukkan bahwa alat dapat berfungsi dengan baik pada lahan seluas 6x5 meter, memberikan informasi kelembapan tanah secara akurat dan realtime dapat diakses melalui telegram.
Penguatan Kemandirian Ekonomi bagi Masyarakat Disabilitas di PPDK Klaten melalui Digitalisasi UMKM Disabilitas dan Penguatan Kewirausahaan Susilo, Dahlan; Ruswanti, Diyah; Cahyani, Rusnandari Retno
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.4742

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan untuk memperkuat kemandirian ekonomi penyandang disabilitas di Perkumpulan Penyandang Disabilitas Klaten (PPDK) melalui digitalisasi UMKM disabilitas dan penguatan kewirausahaan. Permasalahan utama mitra meliputi keterbatasan keterampilan digital marketing, rendahnya kualitas visual produk, serta minimnya pemanfaatan media digital sebagai sarana pemasaran. Metode pelaksanaan dilakukan melalui tahapan persiapan, pelaksanaan, dan evaluasi dengan pendekatan partisipatif berupa pelatihan, pendampingan, diskusi interaktif, serta praktik langsung pemasaran digital menggunakan media sosial dan marketplace. Peserta kegiatan berjumlah 15 orang penyandang disabilitas pelaku UMKM. Hasil kegiatan menunjukkan bahwa 90% peserta mengalami peningkatan pengetahuan dan keterampilan digital marketing, terjadi peningkatan penggunaan media promosi khususnya TikTok dan Facebook, serta peningkatan motivasi kewirausahaan. Program ini memberikan dampak positif terhadap peningkatan daya saing produk UMKM disabilitas dan membuka peluang pengembangan usaha secara berkelanjutan.
COMPARISON OF PRINCIPAL COMPONENT ANALYSIS AND RANDOM FOREST ALGORITHM FOR PREDICTING HOUSING PRICES Susilo, Dahlan; Diyah Ruswanti; Supriyanta; Wawan Nugroho
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.7256

Abstract

House price predictions are an important thing in the property industry and are useful for buyers in making decisions. Principal Component Analysis (PCA) and Random Forest (RF) methods were used for accuracy analysis in predicting housing prices. Purpose of this research is to measure the accuracy of both methods also to compare RF method optimized with PCA and the one that has not been optimized. The data used is house prices in Karanganyar city based on data scraping results on the rumah123.com site. The analysis reveals that Jaten has the highest number of house sales, and sales of houses with land ownership certificates are also the highest. Of the 10 variables used, land area and buildings have the most influence on selling prices. The model training results show that the RF and PCA methods combination has more optimal value than only using the RF method. The error rate of the PCA method is smaller, averaging 0.0257, making its value more consistent than using only the RF method, which has a larger error value with an average of 0.0332. The model training time using PCA is faster (5005.75) than only using the RF method (6099.25)
Co-Authors Anwariningsih , Sri Huning Arif Yulianto Ashsidiq, Muhammad Fadhlan Ravi Astri Charolina Astri Charolina Astri Charolina Charolina, Astri Dahlan Susilo Dwi Retnoningsih Endritha Pramudya Firdhaus Hari Saputro Al Haris Fitriyadi, Farid Hardika Khusnuliawati, Hardika Herawati, Fadhilah Iqbal Aditya Ferryanto Khusnuliawati, Hardika Muhammad Dwi Nugroho Muhammad Dwi Nugroho Noor Arief Kurniawan Nur’Afnida, Widya Prawesti, Nindia Fika Rizky Purnama, Christian Roy Rusnandari Retno Cahyani Supriyanta Wawan Nugroho