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All Journal Dinamik Jurnal Simetris Telematika Jurnal Transformatika Jurnal Informatika Upgris Jurnal Sains dan Informatika Jurnal Pengembangan Rekayasa dan Teknologi JSiI (Jurnal Sistem Informasi) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Sistem Informasi dan Informatika (SIMIKA) Abdimasku : Jurnal Pengabdian Masyarakat Indonesian Journal of Data and Science Aiti: Jurnal Teknologi Informasi JUSTIN (Jurnal Sistem dan Teknologi Informasi) Malcom: Indonesian Journal of Machine Learning and Computer Science Tematik Jurnal Informatika: Jurnal Pengembangan IT Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) The Indonesian Journal of Computer Science Advance Sustainable Science, Engineering and Technology (ASSET) Systematic Literature Review Journal Jurnal DIMASTIK Indonesian Journal of Infomatics
Nurtriana Hidayati
Universitas Semarang
Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Nursing Public Health Social Sciences Transportation Other

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A Systematic Review on Data Fusion Techniques for Agri-cultural Yield Prediction: Integrating Satellite Imagery with Climatic Data Khoirudin, Khoirudin; Pungkasanti, Prind Triajeng; Hidayati, Nurtriana
Systematic Literature Review Journal Vol. 1 No. 4 (2025): October: Systematic Literature Review Journal
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/slrj.v1i4.245

Abstract

An answer to the worldwide need for solutions to food security, data fusion technology that combines climate data with satellite imagery greatly improves the accuracy of agricultural yield predictions; this study intends to examine the advancements, methods, and key contributions of this area. By sifting through 62 papers pulled from Scopus, this research employs the SLR methodology. Document type, data source, open access, subject area, and year of publication (2020–2024) are some of the categories filtered through by Boolean keywords in the selection process. To assess patterns in publications, the efficacy of machine learning models, and key contributions, bibliometric analysis was performed. An upward tendency in publication has been identified by the analysis, particularly beyond the year 2023. Integrating geographical and temporal data has been a great success with machine learning models like Random Forest, Random Forest, and Gradient Boosting. Data resolution, integration of data from several sources, and a real-time framework are still missing pieces to the puzzle when it comes to generalizing research outcomes. More complex data fusion approaches, multiregional datasets, and advanced machine learning models to back more accurate agricultural predictions are all things that this study notes as needing additional investigation in the future. To further innovate agricultural yield prediction, multidisciplinary collaboration is also crucial.
Evaluasi Kinerja Model Long Short-Term Memory dan Gated Recurrent Unit untuk Prediksi Magnitude Gempa Bumi Di Indonesia Nugraha, Giananda Saktika; Priyambodo, Pamungkas Haryo; Rahmayuna, Novita; Hidayati, Nurtriana
Dinamik Vol 31 No 1 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i1.10375

Abstract

This study aims to evaluate and compare the performance of two neural network architectures under the Recurrent Neural Network (RNN) category, namely Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM), in predicting earthquake magnitude in Indonesia. The dataset used consists of daily earthquake magnitude records from 2008 to 2023, preprocessed into time series format and normalized using the MinMax method. The training process was conducted using various combinations of batch size and epoch, and evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and relative prediction accuracy. The evaluation results show that LSTM with a batch size of 32 and 50 epochs provides the best prediction performance, achieving a MAE of 0.2227 and 93.65% accuracy. Meanwhile, GRU performed optimally at a batch size of 64 and 50 epochs, with a MAE of 0.2229 and 93.66% accuracy. The prediction visualization shows that LSTM offers greater stability and precision in tracking actual data patterns. These findings indicate that LSTM holds stronger potential for supporting earthquake prediction systems based on time series data.
ESTIMASI LAJU PERTUMBUHAN PENDUDUK DI KABUPATEN JEPARA DENGAN PENDEKATAN REGRESI LINIER BERGANDA Hetta Rachma; Mutiatun Nafisah; Nurtriana Hidayati
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 1 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v8i1.3790

Abstract

One of the main factors in planning the development of a region is the change in population. A rapid population growth rate can have adverse effects, such as poverty, unemployment, and a decrease in the quality of human resources. The purpose of this study is to predict the population growth rate in Jepara Regency by applying the multiple linear regression method, which can be used as a basis for the government in planning strategies to improve the economy, infrastructure, and other sectors, so that in the end it can have an impact on improving the welfare and quality of life of the community. The data used was obtained from BPS Jepara Regency for the period 2019-2023. Multiple linear regression analysis was applied as the method, with the number of males (X1) and females (X2) as independent variables, and the total population (Y) as the dependent variable. Based on the analysis, the predicted population in 2024 is 1,264,598 people, the same number as in 2023, indicating a stagnant growth condition. The results of this study provide important implications for government policy planning, especially in infrastructure development strategies, economic improvement, and human resource processing. The multiple linear regression method is effective in providing an accurate estimation picture, and can be the basis for future decision-making.
Analisis dan Pengembangan Sistem Informasi Kemahasiswaan Berbasis E-Letter dengan Menggunakan User Centered Design Karunia, Reiza Dwi; Hidayati, Nurtriana
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.3755

Abstract

SISKA adalah sebuah sistem informasi yang memuat mengenai informasi dan kegitan Kemahasiswaan Universitas Semarang. SISKA Sudah berjalan 2 tahun, berdasarkan pengguna SISKA masih memiliki kekurangan danbelum sesuai kebutuhan. Untuk mengetahui ketepatan tersebut sesuai atau tidak, maka disebarlah kuisioner terhadap 26 Responden yang mana terdiri dari Admin dan Mahasiswa yang memiliki hak akses SISKA pada Organisasi Mahasiswa. Dalam organisasi mahsiswa SISKA sebagai tempat mengupload surat menyurat, proposal kegiatan maupun Laporan Pertanggung jawaban kegiatan. Proposal ini dibuat untuk menganalisis serta mengembangkan SISKA agar sesuai dengan Kebutuhan. Pada proses pengembangan sistem ini menggunakan metode User Centered Design (UCD) serta untuk Analisis sistemnya menggunakan perhitungan Kuisioner SUS.
Integrating Multimodal Data Processing Techniques to Enhance User Experience Evaluation in Interactive Digital Platforms Khoirudin Khoirudin; Nurtriana Hidayati
Indonesian Journal of Infomatics Vol. 1 No. 1 (2026): February: Indonesian Journal of Infomatics
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/iji.v1i1.26

Abstract

User experience (UX) evaluation plays a crucial role in understanding how users interact with digital platforms and in improving product design. Traditional UX evaluation methods, such as surveys and interaction logs, often rely on a single data source, which limits the depth of analysis. This study explores the integration of multimodal data processing techniques in UX research, aiming to enhance the accuracy and comprehensiveness of UX evaluations. By combining interaction logs, visual attention data, and physiological measurements, this approach provides a more holistic understanding of user behavior, emotional responses, and satisfaction. Interaction logs offer objective data on user actions, while eye-tracking and physiological data capture users' emotional states, providing richer insights into usability and user experience. This study highlights the effectiveness of multimodal integration in identifying patterns that traditional methods overlook, such as emotional responses to interface elements and real-time feedback from users. The findings reveal that multimodal data processing improves the precision of UX assessment by combining objective behaviors with subjective emotional responses, offering a more complete view of user interactions. The study also discusses the challenges of data synchronization and the potential ethical concerns related to the use of physiological data. The integration of these data sources shows great potential for enhancing the design process, allowing designers to make informed decisions based on comprehensive insights. Finally, this research underscores the future potential of multimodal analytics in UX research, suggesting further exploration of additional data modalities and real-time applications in various digital environments.
Dinamika Spasio-Temporal Korelasi Pendorong Suhu Permukaan Lahan (LST) dan Polusi Nitrogen Dioksida (NO2) di Kota Semarang (2015–2024) menggunakan Google Earth Engine (GEE) Muhammad Andreanto; Agusta Praba Ristadi Pinem; Nurtriana Hidayati
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10431

Abstract

Penelitian ini mengeksplorasi dinamika spasial-temporal faktor lingkungan yang memengaruhi suhu permukaan lahan (LST) di Kota Semarang selama 2015–2024. Tujuan utama adalah mengukur peran vegetasi (NDVI) sebagai pendorong Urban Heat Island (UHI) dan keterkaitan LST dengan polutan NO₂. Metode analisis spasial berbasis Google Earth Engine (GEE) digunakan untuk mengolah data multi-sensor dari Landsat 8/9 (untuk LST dan NDVI) serta Sentinel-5P TROPOMI (untuk NO₂). Korelasi Pearson (r) dihitung dari 1500 titik sampel acak yang diekstrak di GEE, dengan visualisasi raster dan scatter plot untuk interpretasi. Hasil menunjukkan penguatan korelasi negatif antara LST dan NDVI, dari r = -0.322 (R² = 10.4%) pada 2015 menjadi r = -0.362 (R² = 13.1%) pada 2024, yang mengindikasikan sensitivitas termal kota semakin bergantung pada tutupan vegetasi akibat urbanisasi. Sebaliknya, korelasi LST-NO₂ pada 2024 sangat lemah (r = +0.177, R² = 3.1%), membuktikan bahwa polusi didorong emisi transportasi independen dari faktor termal. Studi ini berkontribusi pada pemahaman UHI di kota pesisir tropis, dengan rekomendasi kebijakan terpisah: konservasi Ruang Terbuka Hijau (RTH) untuk mitigasi UHI dan pengendalian emisi untuk NO₂, mendukung adaptasi iklim berkelanjutan.
Analisis Korelasi Curah Hujan dan Lahan Terbangun Terhadap Luasan Genangan Banjir di Kabupaten Demak Mikael Arvito Kurnia Adi; Agusta Praba Ristadi Pinem; Nurtriana Hidayati
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 2 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i2.10392

Abstract

Demak Regency is a low-lying coastal area that faces two types of pressures due to natural phenomena and massive human activities. The purpose of this study is to examine changes in built-up land and measure the level of spatial relationship between rainfall and built-up land area with flood inundation area in Demak Regency during the period 2020 to 2024. The method used in this study is spatial quantitative based on cloud computing using the Google Earth Engine platform that utilizes CHIRPS data for rainfall, Dynamic World for built-up land, and Sentinel-1 for flood inundation area. The findings of this study indicate that the annual rainfall trend has a low level of correlation with a coefficient of determination ranging from 1% to 7% while changes in built-up land show a high level of correlation with a Pearson correlation value between -0.52 to -0.58 which indicate that the reduction of non-built-up land due to land conversion is directly propotional  to the expansion of inundation which contributes to the variation of flood inundation by around 34%. The increase in flood inundation area reaching ±6,970 Ha in 2024 amid normal rainfall confirms that the decline in environmental capacity occurs due to excessive land conversion. This study concludes that managing the risk of increased flooding in Demak Regency requires strict integration of spatial planning policies, not just relying on a meteorology-based early warning system.
ANALISIS PROGRAM MAKAN BERGIZI GRATIS DENGAN SUPPORT VECTOR MACHINE (SVM) PADA APLIKASI X Sekar Cinta Amaria; Nurtriana Hidayati
Jurnal Sistem Informasi Vol. 12 No. 2 (2025)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsii.v12i2.10708

Abstract

The Free Nutritious Meal Program (MBG), which represents a priority program of President Prabowo Subianto, has garnered widespread attention from Indonesian society. This program has received sympathy from various groups, including students and informal workers, and has been extensively discussed through social media, particularly on platform X. This research aims to analyze public response in the form of positive and negative sentiment toward the MBG Program based on data from platform X. A total of 1,378 tweets were collected using crawling methods, followed by preprocessing, sentiment labeling using the InSet lexicon dictionary, and feature extraction using three techniques: Term Presence, Bag of Words (BoW), and Term Frequency-Inverse Document Frequency (TF-IDF). Subsequently, sentiment classification was performed using the Support Vector Machine (SVM) algorithm for each feature extraction technique. Classification results demonstrate that the TF-IDF technique achieved the highest accuracy of 77.5%, compared to Term Presence (76.2%) and BoW (75.3%). Validation using K-Fold Cross Validation with five iterations was conducted with imbalanced data handling through the Synthetic Minority Over-sampling Technique (SMOTE) method. In this validation, TF-IDF consistently demonstrated superior performance with an average accuracy of 75.54%, precision of 74.31%, recall of 73.86%, and f1-score of 73.98%. Despite a slight decrease in accuracy following data synthesis, the TF-IDF technique proved to be stable and effective in handling data variation. The superiority of the TF-IDF feature extraction technique is suitable for combination with the SVM algorithm.
IMPLEMENTASI FUZZY C-MEANS DALAM PENGELOMPOKAN TINGKAT KEMISKINAN PADA KABUPATEN/KOTA DI PROVINSI JAWA TENGAH Rinaldo Dwi Faturahman; Nurtriana Hidayati
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 1 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i1.5747

Abstract

Kemiskinan merupakan salah satu permasalahan bagi negara berkembang khususnya di Indoensia. Setiap tahunya kemiskinan di Provinsi Jawa Tengah terdapat kenaikan ataupun penurunan. Kemikinan di Provinsi Jawa Tengah cukup tinggi. Dibuktikan pada data Badan Pusat Statistik Provinsi Jawa Tengah kemiskinan pada tahun 2022 sebesar 10,93% dan pada tahun 2021 sebesar 10,77%. Tujuan dari penelitian ini untuk mengklasifikasikan tingkat kemiskinan di Provinsi Jawa Tengah dengan metode clustering dengan menggunakan fuzz c means. clustering sendiri adalah salah satu teknik data mining. Dimana data ini merupakan sebuah metode dari data mining untuk mengelompokan data menjadi beberapa kelompok berbeda berdasarkan karakteristik yang sama. Data penelitian yang dugunakan diambil dari Badan Pusat Statistik Provinsi Jawa Tengah  dari tahun 2021 – 2023 dengan total 106 data dan 5 atribut indikator kemiskinan yaitu garis kemiskinan(Rp/kapita/bln), jumlah penduduk miskin(ribu jiwa), pengeluaran, rata-rata pendidikan dan jumlah pengangguran. Hasil penelitian ini  menghasilkan 5 cluster dengan beberapa data di dalamnya. Pada cluster 0 dengan tingkat kemiskinan rendah terdapat 32 kabupaten/kota. Kemudian pada cluster 1 terrdapat 16 kabupaten/ kota dengan tingkat kemiskinan tinggi. Cluster 2 terdapat 13 kabupaten/kota dengan tingkat kemiskinan sangat tinggi. cluster 3 dengan 22 kabupaten/kota dengan kemsikinan yang sangat rendah. Dan cluster 4 dengan 22 kabupaten/kota dengan kemsikinan yang sedang
PENERAPAN AI DAN MANAJEMEN REFERENSI DALAM MENINGKATKAN MUTU KARYA TULIS ILMIAH SISWA SMA MASEHI 2 PSAK SEMARANG Prind Triajeng Pungkasanti; Fajriannoor Fanani; Basworo Ardi Pramono; Nurtriana Hidayati
Jurnal DIMASTIK Vol. 4 No. 2 (2026): Juli
Publisher : Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/dimastik.v4i2.15904

Abstract

Siswa SMA Masehi 2 PSAK Semarang masih belum menguasai teknik penulisan karya tulis ilmiah (struktur, sitasi, dan daftar pustaka) serta belum optimalnya pemanfaatan perangkat digital untuk mendukung proses kepenulisan. Kegiatan Pengabdian kepada Masyarakat (PkM) ini bertujuan untuk menguatkan literasi akademik siswa melalui pelatihan penyusunan karya tulis ilmiah yang terintegrasi dengan manajemen referensi serta pemanfaatan tools Artificial Intelligence (AI) secara etis dan efektif. Metode pelaksanaan dirancang dalam format workshop interaktif berbasis praktik langsung. Materi kegiatan mencakup pelatihan penggunaan aplikasi manajemen referensi serta pemanfaatan AI writing assistant untuk penyuntingan, pengembangan gagasan, dan perbaikan kualitas naskah dengan tetap memperhatikan kaidah akademik. Sebagai instrumen keberlanjutan, PkM ini juga menyediakan modul pembelajaran digital bagi siswa. Hasil dan luaran kegiatan menunjukkan adanya peningkatan kompetensi siswa dalam penulisan karya ilmiah. Selain itu, luaran PkM ini telah menghasilkan produk berupa modul pelatihan digital, publikasi media daring, serta video dokumentasi kegiatan. Melalui PkM ini, siswa menjadi lebih adaptif terhadap perkembangan teknologi pendidikan sekaligus memperkuat budaya literasi akademik serta penggunaan AI yang bertanggung jawab di lingkungan sekolah. Kata Kunci: Literasi AI, Literasi Akademik, Karya Tulis Ilmiah, Manajemen Referensi
Co-Authors Agus Hartanto Agusta Praba Ristadi Pinem Alauddin Maulana Hirzan April Firman Daru Astuti, Rizka Yuli Atmoko Nugroho Aziza, Luthfiah Nur Badroe Zaman Basworo Ardi Pramono Cholil, Saifur Rohman Edi Widodo, Edi Eka Putri Rachmawati Ericx Cahaya Pratama Fajriannoor Fanani Henny Indriyawati Hetta Rachma Irfan, Muhammad Nur Joko Suntoro Karunia, Reiza Dwi Khoirudin Khoirudin Khoirudin Khoirudin, Khoirudin Kholidin Kholidin Laras Elza Devila Lenny Margaretta Huizen Maulana, Bagas Akbar Maulana, Daffa Mifthahul Pratama Weulartafella Mikael Arvito Kurnia Adi Muhammad Andreanto Muhammad Basyier Ardima Muhana Fadhillah Aryash Mutiatun Nafisah Nugraha, Giananda Saktika Nur Wakhidah Prind Triajeng Pungkasanti, Prind Triajeng Priyambodo, Pamungkas Haryo Rachma, Hetta Rahmayuna, Novita Rinaldo Dwi Faturahman Sekar Cinta Amaria Sekar Elok Widhianti Setiaji, Galet Guntoro Susanto, Susanto Suwari, Dewi Ratna Titis Handayani Vensy Vydia Victor Gayuh Utomo Wahyu Maulana Hassanudin Winarti, Titin