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All Journal Jurnal Studi Manajemen Organisasi Telematika : Jurnal Informatika dan Teknologi Informasi Jiko (Jurnal Informatika dan komputer) International Journal of Advances in Data and Information Systems Journal of Advanced in Information and Industrial Technology (JAIIT) Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika SKANIKA: Sistem Komputer dan Teknik Informatika Nusantara Science and Technology Proceedings Mitra Akademia: Jurnal Pengabdian Masyarakat JoMMiT : Jurnal Multi Media dan IT International Journal of Data Science, Engineering, and Analytics (IJDASEA) Jurnal Abdimas Kartika Wijayakusuma International Journal Of Computer, Network Security and Information System (IJCONSIST) ALINIER: Journal of Artificial Intelligence & Applications Journal of International Conference Proceedings Jurnal Pengabdian Masyarakat Bangsa Software Development Digital Business Intelligence and Computer Engineering Jurnal ilmiah teknologi informasi Asia Jurnal Aplikasi Sains Data
Fahrudin, Tresna Maulana
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Implementation of Transfer Function ARIMA Model for Stock Price Prediction Azizah, Alisa Jihan; Prasetya, Dwi Arman; Hindrayani, Kartika Maulida; Fahrudin, Tresna Maulana
International Journal of Advances in Data and Information Systems Vol. 6 No. 2 (2025): August 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i2.1396

Abstract

Dynamic economic growth requires stable financing sources, one of which is through the capital market. In stock investment activities, risk and return are two fundamental aspects that are interrelated and must be carefully considered. The volatility of ASII stock prices, influenced by various factors including exchange rates, can create uncertainty in investment decision-making. This study aims to predict the stock price of PT Astra International Tbk (ASII) using a transfer function model approach that integrates the influence of the Indonesian rupiah to US dollar exchange rate as an external variable. The transfer function model is an extension of the ARIMA model that can measure the dynamic relationship between input and output variables. Based on the estimation results, the best model obtained has a transfer function order of (b,s,r) = (1,0,0) with a noise series of (p_n,q_n) = (1,0). The prediction results show that ASII stock price movements tend to be stable with a gradual decline over the next 20 days. Model evaluation demonstrates low error rates, with MAE of 84.19, RMSE of 110.37, and MAPE of 1.65%. These results indicate that the transfer function model is effective in modeling and predicting short-term stock prices with reasonably good accuracy.
Daily Forecasting for Antam's Certified Gold Bullion Prices in 2018-2020 using Polynomial Regression and Double Exponential Smoothing Fahrudin, Tresna Maulana; Riyantoko, Prismahardi Aji; Hindrayani, Kartika Maulida; Diyasa, I Gede Susrama Mas
Journal of International Conference Proceedings Vol 3, No 4 (2020): Proceedings of the 8th International Conference of Project Management (ICPM) Mal
Publisher : AIBPM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32535/jicp.v3i4.1009

Abstract

Gold investment is currently a trend in society, especially the millennial generation. Gold investment for the younger generation is an advantage for the future. Gold bullion is often used as a promising investment, on other hand, the digital gold is available which it is stored online on the gold trading platform. However, any investment certainly has risks, and the price of gold bullion fluctuates from day to day. People who invest in gold hopes to benefit from the initial purchase price even if they must wait up to five years. The problem is how they can notice the best time to sell and buy gold. Therefore, this research proposes a forecasting approach based on time series data and the selling of gold bullion prices per gram in Indonesia. The experiment reported that Holt’s double exponential smoothing provided better forecasting performance than polynomial regression. Holt’s double exponential smoothing reached the minimum of Mean Absolute Percentage Error (MAPE) 0.056% in the training set, 0.047% in one-step testing, and 0.898% in multi-step testing.
Implementation of Web Scraping on Google Search Engine for Text Collection Into Structured 2D List Fahrudin, Tresna Maulana; Riyantoko, Prismahardi Aji; Hindrayani, Kartika Maulida
Telematika Vol 20 No 2 (2023): Edisi Juni 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i2.9575

Abstract

Purpose: This research proposes the implementation of web scraping on Google Search Engine to collect text into a structured 2D list.Design/methodology/approach: Implementing two important stages in the process of collecting data through web scraping, namely the HTML parsing process to extract links (URL) on Google Search Engine pages, and HTML parsing process to extract the body text from website pages on each link that has been collected.Findings/result: The inputted query is adjusted to the latest issues and news in Indonesia, for example the President's important figures, the month of Ramadan and Idul Fitri, riots tragedy (stadium) and natural disasters, rising prices of basic commodities, oil and gold, as well as other news. The least number of links obtained was 56 links and the most was 151 links, while the processing time to obtain links for each of the fastest queries was 1 minute 6.3 seconds and the longest was 2 minutes 49.1 seconds. The results of scraping links from these queries were obtained from Wikipedia, Detik, Kompas, the Election Supervisory Body (Bawaslu), CNN Indonesia, the General Election Commission (KPU), Pikiran Rakyat, and others.Originality/value/state of the art: Based on previous research, this study provides an alternative to produce optimal collection of links and text from web scraping results in the form of a 2D list structure. Lists in the Python programming language can store character sequences in the form of strings and can be accessed using index keys, and manipulate text efficiently.
Social Media Analysis and Topic Modeling: Case Study of Stunting in Indonesia Muhaimin, Amri; Fahrudin, Tresna Maulana; Alamiyah, Syifa Syarifah; Arviani, Heidy; Kusuma, Ade; Sari, Allan Ruhui Fatmah; Lisanthoni, Angela
Telematika Vol 20 No 3 (2023): Edisi Oktober 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i3.10797

Abstract

Purpose: Stunting is a problem that currently requires special attention in Indonesia. The stunting rate in 2022 will drop to 21.6%, and for the future, the government has set a target of up to 14% in 2024. Rapid technological developments and freedom of expression on the internet produce review text data that can be analyzed for evaluation. This study analyzes the text data of Twitter users' reviews on stunting. The method used is a text-mining approach and topic modeling based on Latent Dirichlet Allocation.Design/methodology/approach: The methodology used in this study is Latent Dirichlet Allocation. The data was collected from twitter with the keyword 'stunting'. After, the data was cleaned and then modeled using the Latent Dirichlet Allocation.Findings/results: The results show that negative sentiment dominates by 60.6%, positive sentiment by 31.5%, and neutral by 7.9%. In addition, this research shows that 'children', 'decrease', 'number', 'prevention', and 'nutrition' are among the words that often appear on stunting.Originality/value/state of the art: This study uses the keyword stunting and analyzes it. Social media analytics show that the people of Indonesia are primarily aware of stunting. Also, the Latent Dirichlet Analysis can be used to create the model.
Analisis Video Keluhan Pelanggan Menggunakan Automatic Speech Recognition dan Analisis Polaritas Sentimen Fahrudin, Tresna Maulana; Aryananda, Rangga Laksana; Gunawan, Ellexia Leonie; Belardo, Valentino; Marcelia, Firsta; Halim, Christina
Software Development, Digital Business Intelligence, and Computer Engineering Vol. 1 No. 1 (2022): SESSION (SEPTEMBER)
Publisher : Politeknik Negeri Banyuwangi Jl. Raya Jember km. 13 Labanasem, Kabat, Banyuwangi, Jawa Timur (68461) Telp. (0333) 636780

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57203/session.v1i1.2022.14-21

Abstract

Tingkat kepuasan pelayanan pelanggan dapat ditinjau berdasarkan keluhan-keluhan pelanggan. Begitu besarnya potensi transaksi penjualan produk dengan pelanggan melalui e-commerce juga meningkatkan peluang terjadinya komplain atau keluhan pelanggan terkait kecacatan produk, keterlambatan produk, kualitas produk, dan lainnya. Keluhan pelanggan biasa disampaikan melalui ulasan-ulasan di media sosial berbentuk teks. Namun, data keluhan pelanggan saat ini semakin bervariasi dalam bentuk video. Oleh karena itu, penelitian ini mencoba untuk menganalisis video keluhan pelanggan menggunakan automatic speech recognition dan analisis polaritas sentimen. Hasil eksperimen menunjukkan bahwa telah ditemukan beberapa keluhan pelanggan pada video yang dianimasikan bertempat di restoran dan mini market. Nilai compound pada video keluhan pelanggan di restoran pada potongan video ke-7 sebesar -0.4747, potongan video ke-10 sebesar -0.8664, dan potongan video ke-11 sebesar -0.6801, sedangkan nilai compound pada video keluhan pelanggan di mini market pada potongan video ke-1 sebesar -0.1027, potongan video ke-2 sebesar -0.2023, dan potongan video ke-5 sebesar -0.5563. Nilai compound tersebut merepresentasikan keluhan pelanggan yang mengarah ke sentimen negatif.
Statistical Analysis of Infant Malnutrition Cases in North Sumatra Before and After COVID-19 Using the Wilcoxon Test Sitanggang, Desi Daomara; Putri, Serlinda Mareta; Agustin, Sesillia; Prasetya, Dwi Arman; Fahrudin, Tresna Maulana
Jurnal Aplikasi Sains Data Vol. 1 No. 2 (2025): Journal of Data Science Applications.
Publisher : Program Studi Sains Data UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/jasid.v1i2.16

Abstract

Child malnutrition remains a very important public health issue in Indonesia. Malnutrition is a condition of deficiency in energy and essential nutrients that can lead to impaired physical growth, mental development, and an increased risk of mortality in children. The prevalence of malnutrition among toddlers in Indonesia is still quite high and shows disparities between regions, especially in provinces with high poverty rates. One province of concern is North Sumatra, which, according to data from the Ministry of Health, has had a significant incidence of malnutrition in the last five years. This condition was exacerbated by the emergence of the COVID-19 pandemic at the end of 2019, which has had a major impact on various sectors of life, including family health and economy. The pandemic caused significant disruptions to primary healthcare systems, including a decrease in posyandu activities, immunizations, and monitoring of children's nutritional status. The decline in household income during the pandemic made it difficult for families to meet their balanced nutritional food needs. A UNICEF study showed an increased risk of acute malnutrition in children during the pandemic, especially in previously vulnerable areas. To measure the impact of the COVID-19 pandemic on the incidence of child malnutrition, a statistical approach that can compare data before and after the pandemic is needed. This study aims to analyze the difference in the incidence of child malnutrition before and after the COVID-19 pandemic in North Sumatra Province using the Wilcoxon test method. Using the Wilcoxon Signed-rank Test statistical method, a comparative analysis was performed between the medians of the data from 2018 and 2023. The results of the study showed that there was a difference between the medians of the two data sets.
Perbandingan Kinerja Algoritma XGBoost dan CatBoost dalam Klasifikasi Risiko Penyakit Diabetes Utomo, Setyobudi; Sigit, Syauqita; Sulistyowati, Niken; Prasetya, Dwi Arman; Fahrudin, Tresna Maulana
ALINIER: Journal of Artificial Intelligence & Applications Vol. 6 No. 2 (2025): ALINIER Journal of Artificial Intelligence & Applications
Publisher : Program Studi Teknik Elektro S1 ITN Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/alinier.v6i2.14772

Abstract

Diabetes merupakan penyakit kronis dengan prevalensi global yang terus meningkat, termasuk di Indonesia. Deteksi dini sangat penting untuk mencegah komplikasi serius yang terkait dengan kondisi ini. Seiring dengan kemajuan teknologi, pendekatan machine learning semakin banyak diterapkan dalam klasifikasi penyakit dan prediksi risiko. Penelitian ini bertujuan untuk membandingkan kinerja dua algoritma gradient boosting, yaitu XGBoost dan CatBoost, dalam mengklasifikasikan risiko diabetes berdasarkan data medis pasien. Dataset yang digunakan adalah Diabetes Dataset, yang terdiri dari delapan fitur medis dan satu label target. Penelitian ini mencakup proses pra-pemrosesan data, pelatihan model dengan pembagian data latih dan uji, serta evaluasi menggunakan metrik klasifikasi seperti akurasi, presisi, recall, dan F1-score. XGBoost dipilih karena efisiensinya dalam komputasi serta adanya regularisasi bawaan yang membantu mencegah overfitting pada data numerik berskala besar. Sementara itu, CatBoost digunakan karena kemampuannya dalam menangani fitur kategorikal secara langsung melalui teknik random permutation dan ordered boosting. Hasil dari penelitian ini diharapkan dapat berkontribusi pada pengembangan sistem prediksi diabetes yang lebih akurat dan efisien serta menjadi referensi dalam penerapan algoritma boosting di bidang medis.
IoT-Based Water Quality Monitoring System to Enhance Sustainability and Business Performance in Koi Fish Cultivation Sugiarto; Nugraha, Isna; Fahrudin, Tresna Maulana; Rizqina, Azza; Agvenia, Keisya
Journal of Advances in Information and Industrial Technology Vol. 7 No. 2 (2025): Nov
Publisher : LPPM Telkom University Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52435/jaiit.v7i2.730

Abstract

Water quality is a critical factor that determines the survival and productivity of koi fish cultivation. Fluctuations in key parameters, such as pH, dissolved oxygen (DO), total dissolved solids (TDS), and turbidity, can induce stress and lead to mass fish mortality, resulting in substantial financial losses for farmers. This study proposes an IoT-based water quality monitoring system designed to enhance both environmental sustainability and business performance in koi aquaculture. The system integrates four sensors (pH, DO, TDS, and turbidity) connected to an ESP32 microcontroller, which transmits real-time data via Wi-Fi to cloud platforms (Firebase and Blynk). A dedicated dashboard provides continuous monitoring, historical trend visualization, and real-time alerts when parameter thresholds are exceeded. The prototype was validated in an operational koi pond and achieved an average accuracy of 96.5%. User testing involving 10 koi farmers showed an 89% satisfaction rate, demonstrating the system's practicality and usability. Economically, the solution reduced manual monitoring costs by 40%, water replacement volume by 25%, and increased fish survival rates by 12%. These results indicate that IoT implementation in aquaculture not only improves environmental control but also increases operational efficiency and overall profitability, contributing to sustainable, data-driven aquaculture practices.
Co-Authors Agustin, Sesillia Agvenia, Keisya Al Makruf, Achmad Yusuf Alamiyah, Syifa Syarifah Alamsyah, Ryan Badai Ardiani, Ardia Eva Ardiani, Fira Arianto, Fachrizal Aryananda, Rangga Laksana Atnanda, Primus Akbar Azizah, Alisa Jihan Belardo, Valentino Damaliana, Aviolla Terza Damayanti, Camilla Alifia Diyasa, I Gede Susrama Mas Dwi Arman Prasetya Gunawan, Ellexia Leonie Halim, Christina Hartono, Verina Dewanti Heidy Arviani Hindrayani , Kartika Maulida Hindrayani, Kartika Maulida idhom, Mohammad Illahi, Zabda Dimas Anugrah Isfatayati, Nur Ita Kurniawati Kamisutara, Made Kusuma, Ade Lestari, Amanda Ayu Dewi Lestari, Rizky Dwi Ayu Lisanthoni, Angela Maharani, Berlian Surya Marcelia, Firsta Much. afif masykur mughni Muhaimin, Amri Niken Sulistyowati Nugraha, Isna Putri, Irma Amanda Putri, Serlinda Mareta Putri, Shafira Amanda Putricia, Andini Mutiara Riskiyah, Ameliyah Riwangga, Rikhi Putra Riyantoko, Prismahardi Aji Rizqina, Azza Sa'diyah, Ilmatus Salsabila, Olivia Santosa, Bernadheta Donabella Ega Sari, Allan Ruhui Fatmah Selayanti, Nabilah Sigit, Syauqita Sitanggang, Desi Daomara Siti Kotijah Sugiarto Tasya, Anindya Nur Trimono, Trimono Trisnawati, Ananda Eka Putri Utari, Komang Mega Ilda Utomo, Setyobudi Varqa Ansori, Nine Alvariqati Yasin, Andrew Arjunanda