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All Journal Information Technology and Telematics Dinamik Jupiter Publikasi Eksternal Jurnal Buana Informatika Pixel : Jurnal Ilmiah Komputer Grafis JUITA : Jurnal Informatika Proceeding SENDI_U Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL ILMIAH INFORMATIKA JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Teknik Informatika UNIKA Santo Thomas INTECOMS: Journal of Information Technology and Computer Science Building of Informatics, Technology and Science Jurnal Teknologi Informasi dan Terapan (J-TIT) Jurnal Manajemen Informatika dan Sistem Informasi Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Teknik Elektro dan Komputasi (ELKOM) JATI (Jurnal Mahasiswa Teknik Informatika) Aiti: Jurnal Teknologi Informasi Dinamika Informatika: Jurnal Ilmiah Teknologi Informasi Jurnal Teknik Informatika (JUTIF) Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Jurnal Informatika Teknologi dan Sains (Jinteks) Maritime Park: Journal Of Maritime Technology and Socienty Jurnal Pengabdian Masyarakat Intimas (Jurnal INTIMAS): Inovasi Teknologi Informasi Dan Komputer Untuk Masyarakat Eduvest - Journal of Universal Studies Seminar Nasional Teknologi dan Multidisiplin Ilmu Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) INOVTEK Polbeng - Seri Informatika
Eri Zuliarso
Universitas Stikubank
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering Neuroscience Social Sciences

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ANALISIS SENTIMEN BERDASARKAN ULASAN GOOGLE PLAY STORE PADA APLIKASI DANA MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM) Putra Alva, Ilyasa Garuda; Zuliarso, Eri
INTECOMS: Journal of Information Technology and Computer Science Vol. 8 No. 5 (2025): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/jyssxh48

Abstract

Penelitian ini bertujuan untuk mengklasifikasikan sentimen dari ulasan pengguna terhadap alikasi DANA yang diperoleh dari Google Play Store menggunakan algoritma Support Vector Machine (SVM). Data dikumpulkan melalui teknik web crawling dan diproses melalui tahapan preprocessing seperti case folding, tokenisasi, filtering, stopword removal, dan stemming. Ulasan kemudian dikonversi menjadi representasi numerik menggunakan metode TF-IDF dan diklasifikasikan ke dalam dua kategori sentimen: positif dan negatif. Evaluasi model dilakukan menggunakan metrik accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa metode SVM memberikan performa klasifikasi yang tinggi dengan nilai accuracy sebesar 88,5%, precision 90,2%, recall 95,1%, dan F1-score 92,5%. Visualisasi hasil dilakukan melalui hitogram distribusi sentimen dan WordCloud untuk menampilkan kata-kata dominan. Penelitian ini memberikan wawasan terhadap persepsi pengguna serta dapat dijadikan dasar pengembangan layanan aplikasi DANA. Kata Kunci: Analisis Sentimen, DANA, Support Vector Machine, TF-IDF, Ulasan Pengguna.
IMPLEMENTASI METODE DESIGN THINKING PADA PERANCANGAN WEBSITE PROFIL DEPARTEMEN TEKNIK KOMPUTER Fidiniari, Fathia; Zuliarso, Eri
INTECOMS: Journal of Information Technology and Computer Science Vol. 8 No. 5 (2025): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/j0chdm90

Abstract

Penelitian ini mengimplementasikan Metode Design Thinking dalam pengembangan website profil Departemen Teknik Komputer untuk meningkatkan pengalaman pengguna dan aksesibilitas. Studi ini menjawab tantangan pembuatan antarmuka yang intuitif dan mudah digunakan bagi pemangku kepentingan akademik. Dengan pendekatan mixed-method, penelitian mengikuti lima tahap design thinking: empati, definisi, ideasi, prototipe, dan pengujian. Data dikumpulkan melalui wawancara mendalam dengan 15 stakeholder, pengujian usability, dan evaluasi System Usability Scale (SUS) melibatkan 30 responden. Hasil penelitian menunjukkan peningkatan signifikan dalam usability dengan skor SUS rata-rata 78,4 (kategori "baik"), membuktikan keberhasilan penerapan prinsip user-centered design. Temuan ini menegaskan efektivitas design thinking dalam menyelesaikan masalah kompleks antarmuka di lingkungan akademik. Penelitian berkontribusi secara teoretis dengan memperkaya literatur tentang design thinking dalam pengembangan website pendidikan, serta secara praktis dengan menyediakan kerangka kerja yang dapat diadopsi institusi serupa. Rekomendasi mencakup pengujian iteratif dan pelibatan stakeholder untuk solusi digital berkelanjutan.   Kata Kunci: Design Thinking, pengembangan website, usability, profil akademik, desain berpusat pengguna
Rainfall Forecasting Using SSA-Based Hybrid Models with LSSVR and LSTM for Disaster Mitigation Ruslana, Zauyik Nana; Zuliarso, Eri
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4963

Abstract

Accurate rainfall forecasting is crucial for addressing the increasing risk of hydrometeorological disasters, particularly in tropical regions such as Semarang City, Indonesia. However, conventional forecasting models often struggle with inaccurate data and observations. This study proposes a novel hybrid combination of SSA-NMF with LSSVR and LSTM, offering high-resolution rainfall forecasting over multiple monitoring stations, to predict daily rainfall. As a preprocessing step, 15 years of daily rainfall data from six observation stations were denoised and decomposed using Singular Spectrum Analysis (SSA) combined with Non-Negative Matrix Factorization (NMF). This approach effectively handled data with many zero values, identified seasonal patterns or high-rainfall locations, and extracted key patterns. The prediction models were trained and validated using parameters optimized through RandomizedSearchCV for LSSVR and Keras Tuner for LSTM. Model performance was evaluated using MSE, RMSE, MAE, and Nash-Sutcliffe Efficiency (NSE). The results showed that the SSA-LSTM model consistently outperformed SSA-LSSVR model, with the highest average NSE value being 0.9 across six monitoring locations in Semarang City. Furthermore, the predicted rainfall values were spatially visualized using Inverse Distance Weighting (IDW) interpolation within a Geographic Information System (GIS) environment, producing informative rainfall distribution maps that support early warning systems and disaster mitigation efforts. In conclusion, the hybrid approach combining SSA-NMF preprocessing with LSTM-based deep learning significantly improves the accuracy and reliability of daily rainfall forecasting. This novel SSA‑NMF + LSSVR/LSTM framework delivers high‑resolution, reliable rainfall forecasts that directly empower disaster risk reduction systems and readily transfer to similar climatic regions.
Perbandingan Kinerja LSTM, Bi-LSTM, dan Prophet untuk Prediksi Kekeringan berdasarkan SPEI (Standardized Precipitation-Evapotranspiration Index) Amalina, Hana; Zuliarso, Eri
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7971

Abstract

Drought is a natural disaster with widespread impacts on agriculture and water availability, particularly in the Gajah Mungkur Reservoir area of Wonogiri Regency, Indonesia. Rainfall instability driven by global climate change and local climate variability is the primary cause of this disaster. Accurate drought prediction is essential for formulating sustainable mitigation strategies. This study aims to analyze drought characteristics in the Gajah Mungkur Reservoir, Wonogiri Regency, using the Standardized Precipitation Evapotranspiration Index (SPEI) and to compare the performance of three prediction models: Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Prophet in predicting SPEI. The dataset includes monthly rainfall and air temperature data from 1995 to 2024. The analysis reveals that longer SPEI time scales tend to show more temporally concentrated drought patterns. At the 6-month SPEI scale, which represents long-term drought, a total of 55 drought months were detected between 1995 and 2024, with major drought episodes occurring in 1996–1997, 2000–2007, 2019, and 2023–2024. Model performance evaluation shows a numerical trend in which Bi-LSTM outperforms others for 1-month SPEI prediction, while LSTM performs better at the 3- and 6-month scales. However, statistical significance testing indicates that the performance differences among the three models are not significant (p > 0,05), suggesting that other factors such as computational efficiency may be important considerations in practical applications.
Decade Rainfall Prediction Using Prophet Algorithm and LSTM (Case Study in Banjarnegara Regency) sulis, Sulistiyowati; Eri Zuliarso
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/j3mbxq89

Abstract

Hydrometeorological disasters such as floods and landslides in Banjarnegara Regency are closely related to fluctuating rainfall variability. This study aims to predict decadal (10-day) rainfall by comparing the performance of the Prophet algorithm and the Long Short-Term Memory (LSTM) model. The dataset comprises daily rainfall records from 14 observation stations spanning the period 2005–2024. The research stages included preprocessing, modelling, hyperparameter optimization using Optuna, and evaluation with Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results indicate that the Prophet model outperformed LSTM in most locations, with an average RMSE of 69.55 and MAE of 53.05, lower than LSTM, which recorded 73.03 and 55.72, respectively. The ensemble averaging model produced competitive results at several locations, although it was less responsive to sharp fluctuations in rainfall. These findings confirm that Prophet is more effective in capturing seasonal patterns and long-term trends, thus providing significant potential to support climate-based disaster mitigation systems in vulnerable areas such as Banjarnegara
Time Series Forecasting for Container Throughput Using SARIMA and LSTM: A Case Study of Tanjung Emas Port, Semarang Ratmoko, Hari; Zuliarso, Eri
Maritime Park: Journal of Maritime Technology and Society Volume 4, Issue 3, 2025
Publisher : Department of Ocean Engineering, Faculty of Engineering, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62012/mp.vi.45866

Abstract

Abstract: Accurate forecasting of container throughput is vital for enhancing strategic planning and operational efficiency in seaport management. This study compares the performance of two time series forecasting models Seasonal Autoregressive Integrated Moving Average (SARIMA) and Long Short-Term Memory (LSTM) in predicting container throughput at Tanjung Emas Port, Semarang, Indonesia. Monthly throughput data from January 2014 to April 2025 were preprocessed using stationarity transformation and normalization techniques. Model performance was evaluated using Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The SARIMA model effectively captured seasonal patterns and produced accurate short-term forecasts. Conversely, the LSTM model exhibited notable significant deviation from the actual data , indicating lower predictive performance in this context. The findings indicate that SARIMA currently provides a more reliable forecasting approach for the port. Future research should consider hybrid models (e.g., SARIMA-LSTM) and incorporate exogenous variables to improve forecasting accuracy and support data-driven decision-making in port operations.
Sistem Rekomendasi Pemilihan Mobil Menggunakan Metode EDAS ariadi, hastomo; Zuliarso, Eri
Jurnal Teknik Elektro dan Komputasi (ELKOM) Vol 2, No 2 (2020): ELKOM
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/elkom.v2i2.22759

Abstract

According to data from the Indonesian Automotive Industry Association, or GAIKINDO, car sales decreased in 2020 and 2021 as a result of the Covid-19 epidemic, but increased by 49% in 2022 and 2023. This demonstrates that, when it comes to personal transportation, cars are preferred by Indonesians over alternative options. In order to choose automobiles based on the brand, model, type, price, engine capacity, transmission, year of manufacture, and gasoline capacity, this research will employ the EDAS approach. A Honda CRV automobile with a value of ASi = 0,812 was the recommendation that emerged from the selection criteria for the Honda brand and CVT gearbox, and a Honda Brio car whit a value of ASi = 0,713 was the second recommedation and a Honda BRV with a value of ASi = 0,6888 was the thrid recommendation. 
Co-Authors . Sulastri . Suwarsito Aditya Bobby Rizki Allaam, Ekananda Naufal Amalina, Hana Anefia Mutiara Atha ariadi, hastomo Arief Jananto Ariyani, Dewi Ayu Arya Sena Setyanegara Astuti, Vivi Rizki Indri Bambang Wiranto, Joko Cahyono Purbomartono Dewi Handayani Untari Ningsih Dian Kristiawan Nugroho Dwi Agus Diartono Dwi Budi Santoso Edy Winarno Fathoni, Aliffatul Majid Februarianti, Herny Fidiniari, Fathia Gunadi, Ilham Hastomo Ariadi Herny Februariyanti Herny Februariyanti Hersatoto Listiyono Hindayati Mustafidah Indah Widhi Prastika Indriani, Cheryllista Isworo Nugroho Khabib Mustofa Kogoya, Erminus Kusuma Satria, Hafiyan Nafan Mardi Siswo Utomo Munna, Aliyatul Nur Rohim Nurmakhlufi, Alfin Hilmy Putra Alva, Ilyasa Garuda Putri, Indah Lissiana R. Soelistijadi Radyanto, Mohammad Riza Rara Sri Artati Redjeki Ratmoko, Hari Rezal Arminto, Edo Rina Candra NS Rizky Abdul Malik Rosyida, Elviana Ruslana, Zauyik Nana S Sunardi Safra, Icha Adellia Safra, Kyla Kaneshia Sariyun Naja Anwar Sri Eniyati Sri Hartati Sudiantoro, Adhi Viky Sugiyamto Sugiyamto, Sugiyamto Sulastri Sulastri Sulastri Sulastri Sulatri sulis, Sulistiyowati Sunardi Sunardi Velamentosa, Desvio Wahyu Prasetyo Wibowo, Sayogo Wismarini, Th Dwiati Yassin Achmad Nur Aziz Yohanes Suhari Yunus Anis Yunus Anis, Yunus Yuwan, Ridho Pangestu Zulfa Febriana Dewi Mellinia