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All Journal Jurnal Transformatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Informatika Upgris E-Dimas: Jurnal Pengabdian kepada Masyarakat RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Sains dan Informatika JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Pengembangan Rekayasa dan Teknologi JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Tekno Kompak Building of Informatics, Technology and Science Jurnal Teknologi Informasi dan Multimedia Journal of Information Systems and Informatics JATI (Jurnal Mahasiswa Teknik Informatika) Abdimasku : Jurnal Pengabdian Masyarakat JITU : Journal Informatic Technology And Communication Aiti: Jurnal Teknologi Informasi Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Sawala : Jurnal pengabdian Masyarakat Pembangunan Sosial, Desa dan Masyarakat Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Algoritme Jurnal Mahasiswa Teknik Informatika Jurnal Pengabdian Masyarakat Intimas (Jurnal INTIMAS): Inovasi Teknologi Informasi Dan Komputer Untuk Masyarakat Jurnal INFOTEL SmartComp Tematik Jurnal DIMASTIK
Nur Wakhidah
Fakultas Teknologi Informasi Dan Komunikasi Universitas Semarang
Religion Arts 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 Library & Information Science Mathematics Mechanical Engineering Social Sciences Transportation

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Optuna-Driven Hyperparameter Optimization in Tsukamoto Fuzzy Logic for House Price Estimation Annisa Aurelia Fitriani; Nabilah Putri Wijaya; Susanto Susanto; Nur Wakhidah
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9573

Abstract

The property sector faces challenges in determining accurate house selling prices due to subjectivity and market uncertainty. The relationship between physical attributes, such as land area and building area, and price is not always linear, making conventional methods often less precise in estimation. This study aims to design a decision support system to objectively estimate house prices in the Plamongan area, Semarang. The method used is Fuzzy Tsukamoto Logic. This preliminary study explores the integration of the Tree-structured Parzen Estimator (TPE) algorithm through the Optuna framework to automatically optimize membership function limits, replacing manual trial and error methods. The dataset was collected via scraping techniques, providing a pilot dataset of 26 data points. Final model performance evaluation showed a Mean Absolute Percentage Error (MAPE) value of 11.39%, which falls into the 'Good Forecast' category. However, given the highly limited sample size, these findings primarily serve as a proof-of-concept that requires further validation with larger, multi-variable datasets. These results prove that integrating the Fuzzy Tsukamoto method with hyperparameter optimization is effective in reducing subjectivity and providing reliable property price estimates. The primary contribution of this research is providing a mathematical proof-of-concept for an automated, objective property valuation system that eliminates human bias in fuzzy parameter configuration, offering a practical baseline tool for localized real estate markets.
Pendekatan Naive Bayes dalam Analisis Sentimen pada Ulasan Pengguna Aplikasi Indodax di Platform Google Play Store Riki Ardi Pranata; Susanto Susanto; Nur Wakhidah
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 15, No 2 (2026): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v15i2.9938

Abstract

Aplikasi investasi digital, seperti Indodax, berperan sebagai sarana transaksi jual beli aset kripto yang mendukung aktivitas pengguna sesuai dengan tujuan dan kebutuhan investasi. Analisis sentimen digunakan untuk mengidentifikasi opini serta kecenderungan sikap pengguna terhadap suatu topik. Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna Aplikasi Indodax yang diperoleh dari platform Google Play Store. Metodologi yang diterapkan adalah Knowledge Discovery in Database (KDD), yang meliputi tahapan data selection, preprocessing, pelabelan berbasis lexicon-based, transformation, klasifikasi menggunakan algoritma Naive Bayes, serta evaluasi. Proses klasifikasi dilakukan untuk mengelompokkan ulasan ke dalam dua kategori sentimen, yaitu positif dan negatif. Dataset penelitian berasal dari ulasan pengguna Play Store yang telah melalui tahap prapemrosesan teks. Hasil pengujian menunjukkan algoritma Naive Bayes memberikan performa klasifikasi yang cukup baik. Berdasarkan tiga skenario pembagian data latih dan data uji, yaitu rasio 60:40, 70:30, dan 80:20, diperoleh rasio 60:40 menghasilkan kinerja optimal dengan nilai akurasi sebesar 82,95% serta nilai presisi, recall, dan F1-score sebesar 83%. Distribusi sentimen menunjukkan 55,45% ulasan bersifat negatif dan 44,55% bersifat positif, menandakan tanggapan pengguna terhadap aplikasi Indodax masih didominasi oleh sentimen negatif. Namun, metode pelabelan berbasis lexicon-based masih kesulitan dalam konteks kalimat seperti sarkasme, bahasa informal, dan ambigu dari sebuah ulasan.
A Robustness-Oriented Evaluation of LSTM, GRU, and Hybrid LSTM-GRU Models for ANTM.JK Stock Price Forecasting Khoirudin; Prind Triajeng Pungkasanti; Nur Wakhidah; Vinay Rishiwal
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1660

Abstract

Accurately forecasting stock prices remains challenging because of the nonlinear and volatile nature of financial markets, particularly during periods of heightened uncertainty, such as the COVID-19 pandemic. This study evaluates the robustness of three models, LSTM, GRU, and Hybrid LSTM-GRU, for ANTM.JK stock price forecasting using a volatility-oriented evaluation framework. Historical stock data from September 2005 to May 2022 were transformed into supervised time-series datasets using a 15-lag sliding window. The model performance was evaluated using baseline prediction accuracy, 5-fold chronological cross-validation consistency, and synthetic stress scenarios consisting of controlled price drops, price rises, and high-volatility noise. Evaluation metrics included RMSE, MSE, MAE, R, and R^2. The GRU model delivered the top baseline prediction results, achieving the smallest RMSE of 52.95 and MAE of 28.14. In cross-validation, the LSTM model recorded the lowest average RMSE of 119.41. Meanwhile, the Hybrid LSTM-GRU exhibited the highest prediction consistency and robustness across various synthetic stress scenarios. In contrast to earlier research that mainly focused on prediction precision, this study presents a comprehensive framework for evaluating robustness. This framework combines baseline accuracy, consistency through cross-validation, and an analysis of synthetic stress scenarios. The generated robustness map offers a systematic interpretation of model strengths across diverse evaluation goals, facilitating a more thorough assessment of stock-forecasting models in different market environments.
Perancangan dan Implementasi Sistem Ticketing Support untuk Pengelolaan Pengaduan Pelanggan Affi Himmawan; Nur Wakhidah
Jurnal Teknologi Informasi dan Multimedia Vol. 8 No. 3 (2026): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v8i3.996

Abstract

CV. Metamorphz Technology Solutions is a company operating in the information technology sector that receives various complaints and service requests from customers in its daily opera-tions. Previously, the customer complaint management process was carried out manually, causing inefficiencies in recording, tracking, and resolving complaint tickets. This condition resulted in slow customer response times and difficulty in monitoring complaint status in realtime. This study aims to implement support ticketing system to manage customer complaints at CV. Meta-morphz Technology Solutions. The research method used is the Agile Development method, which consists of five stages: requirements analysis, system design, implementation, testing, and maintenance. The system was developed using a web-based platform with a SQL Server data-base, equipped with a chatbot feature based on a Natural Language Processing (NLP) algorithm using a Rule-Based and TF-IDF (Term Frequency-Inverse Document Frequency) approach for au-tomatic classification and matching of complaint categories. The chatbot acts as the first line of customer interaction in receiving, classifying, and providing initial responses to complaints before forwarding them to the relevant technical team. The results show that the ticketing sys-tem integrated with the chatbot successfully streamlines the complaint handling process, enabling structured complaint recording, automated ticket assignment to the relevant technical team, and realtime monitoring of complaint status. System testing using the Black-Box Testing method showed that all system features function according to requirements, with a test success rate of 100% from nine testing scenarios conducted. The implementation of this ticketing system im-proves response time and service quality to customers, as well as facilitating performance moni-toring of the technical support team.
PENERAPAN FUZZY ASSOCIATIVE MEMORY UNTUK MENGUKUR TINGKAT KEPUASAN PELANGGAN TERHADAP PELAYANAN PADA TOKO KOPIMA: APPLICATION OF FUZZY ASSOCIATIVE MEMORY TO MEASURE THE LEVEL OF CUSTOMER SATISFACTION REGARDING SERVICE AT THE KOPIMA STORE Nur Wakhidah; Ira Nurul Febriani
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 9 No 2 (2024): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v9i2.4773

Abstract

One of the advantages of information technology in the field of business is that it can help in measuring the level of customer satisfaction. Because measuring customer satisfaction is an important element in providing better, more efficient and more effective services. KOPIMA stores, which is a student cooperative, is experiencing unstable sales problems, this is the result of customer dissatisfaction. There is a system used to measure the level of customer satisfaction with services at KOPIMA stores to find out what are the expectations of customer complaints and how to evaluate service at KOPIMA stores. To measure the level of customer satisfaction with services at KOPIMA stores, the Fuzzy Associative Memory (FAM) method is used. FAM is a flexible decision-making method, which is a fuzzy system that maps fuzzy sets to other fuzzy sets. The output obtained is a web-based system, with the results of the FAM method that can be implemented to calculate customer satisfaction into the rating category, namely 8% satisfied and 92% very satisfied in the November 2022 assessment and December 2022 assessment, namely 4% satisfied and 96% very satisfied . So it can be concluded, customer satisfaction has increased with the assessment criteria being very satisfied with the service at the KOPIMA store.
PREDIKSI CURAH HUJAN DI KOTA SEMARANG MENGGUNAKAN METODE LONG SHORT-TERM MEMORY DAN GATED RECURRENT UNIT Isa Ghani Al-Hadid; Tan Bagas Endrihartono; Nur Wakhidah
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 10 No. 1 (2026): JATI Vol. 10 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v10i1.17064

Abstract

Curah hujan merupakan parameter meteorologis penting yang berpengaruh terhadap mitigasi bencana, perencanaan wilayah, dan pengelolaan sumber daya air, khususnya di Kota Semarang yang memiliki tingkat kerawanan banjir tinggi. Permasalahan utama dalam prediksi curah hujan adalah karakteristik data yang bersifat fluktuatif, nonlinier, serta dipengaruhi oleh berbagai faktor atmosfer, sehingga metode statistik konvensional sering kali belum mampu memberikan hasil yang optimal. Penelitian ini bertujuan untuk membandingkan kinerja model Long Short-Term Memory (LSTM) dan Gated Recurrent Unit (GRU) dalam memprediksi curah hujan harian di Kota Semarang serta menganalisis pengaruh variasi panjang data historis (window size) terhadap akurasi prediksi. Metode yang digunakan meliputi pengolahan data curah hujan harian periode Januari 2016 hingga Desember 2025 dari BMKG, pra-pemrosesan data, normalisasi, pembentukan data sekuensial dengan window size 7, 14, 30, dan 60 hari, serta pelatihan dan evaluasi model LSTM dan GRU menggunakan metrik MAE, MSE, RMSE, dan koefisien determinasi (R²). Hasil penelitian menunjukkan bahwa model GRU memberikan performa terbaik pada window size 30 hari dengan nilai RMSE sebesar 5,655 dan R² sebesar 0,735, sedangkan LSTM menghasilkan performa terbaik pada window size 14 hari dengan R² sebesar 0,726. Secara keseluruhan, GRU menunjukkan kinerja yang lebih stabil dan akurat dalam memprediksi curah hujan harian di Kota Semarang
Perbandingan Decision Tree, KNN, dan Naive Bayes pada Klasifikasi Mood Musik Menggunakan Dataset Emotion Kaggle Miftakhur Rahman; Muhammad Arham Lutfi; Nur Wakhidah
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 16 No 1 (2026): Maret 2026
Publisher : STMIK Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33020/saintekom.v16i1.1019

Abstract

The classification of music mood characteristics is a crucial instrument in Music Information Retrieval (MIR) systems to support recommendation technology and AI-based emotion analysis. This study aims to evaluate and compare the performance of three classification algorithms: Decision Tree, K-Nearest Neighbors (KNN), and Naive Bayes. The dataset utilized is sourced from the Kaggle Emotion Dataset, comprising 1,440 audio files. The feature extraction process was conducted using the Librosa library to capture acoustic parameters, including Mel-Frequency Cepstral Coefficients (MFCC), Delta-MFCC, Chroma, Spectral Contrast, Spectral Centroid, Spectral Bandwidth, and Tempo. All features were normalized using StandardScaler and distributed into training and testing sets with an 80:20 ratio. Based on the experimental results, the K-Nearest Neighbors algorithm demonstrated the most superior performance with an accuracy of 71.52%. Meanwhile, the Decision Tree algorithm achieved an accuracy of 54.16%, and Naive Bayes obtained 53.47%. The primary contribution of this research is the empirical evidence of the effectiveness of distance-based algorithms in identifying emotional patterns within multidimensional audio data. These findings provide a robust methodological reference for the future development of music emotion recognition systems
Komparasi Metode SVM Dan Random Forest Pada Analisis Sentimen Ulasan Aplikasi Open AI Zuli Chofifah; Nur Wakhidah
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 10, No 2 (2025): Edisi Agustus
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v10i2.925

Abstract

In this study, the sentiment analysis of ChatGPT application reviews gathered from the Google Play Store is compared using the Support Vector Machine (SVM) and Random Forest techniques. The google-play-scraper package was used to scrape for the dataset. The data was subjected to a number of preparation procedures before categorization, such as text normalization, stopword removal, character removal, and stemming using the Sastrawi package. After classifying each review using both algorithms, the sentiment of each review was labeled according to its rating score. According to the experimental results, Random Forest attained an accuracy of 94.00%, whereas SVM achieved 95.00%. According to these results, SVM performs marginally better than Random Forest at identifying the sentiment of user reviews of OpenAI applications.
Optimalisasi Platform TikTok sebagai Media Pembelajaran Interaktif di SMK Nusaputera 1 Semarang: Panduan Membuat Konten Atraktif dan Beretika Angela Baptista Bernadine Frederica; Rizqy Dhafin Son Hajee; Henri Septa Maulana; Yosua Danny Santana; Sekar Ayu Rahmawati; Miftakhur Rahman; Akmal Rizqan Rafliansyah; Nur Wakhidah
Jurnal DIMASTIK Vol. 3 No. 2 (2025): Juli
Publisher : Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/dimastik.v3i2.12639

Abstract

Maraknya penggunaan TikTok di kalangan siswa SMK Nusaputera 1 Semarang sering kali diiringi kesulitan dalam menciptakan konten yang menarik, edukatif, dan profesional, alih-alih terjebak pada tren "cringe". Pengabdian masyarakat ini berfokus pada peningkatan keterampilan digital siswa untuk memproduksi konten TikTok yang kreatif, informatif, dan positif. Melalui pelatihan dan pendampingan intensif, siswa diajarkan konsep TikTok efektif, teknik video, editing dasar, hingga strategi narasi yang kuat. Hasilnya, terjadi peningkatan signifikan dalam kemampuan siswa membuat konten TikTok yang orisinil, berkualitas, dan relevan, mengubah "cringe" menjadi peluang ekspresi diri yang produktif. Kata Kunci: TikTok Edukatif, Keterampilan Digital, Pengabdian Masyarakat Konten Kreatif, SMK Nusaputera 1 Semarang
Sistem Pendukung Keputusan Penerima Bantuan Sosial dengan AHP dan MOORA Setiawan Adi Nugroho; Nur Wakhidah
Jurnal Transformatika Vol. 23 No. 1 (2025): July 2025
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v23i1.12244

Abstract

Poverty is a multidimensional problem that requires prompt and appropriate handling to maintain a dignified human life. In Manyaran Sub-district, Semarang City, the distribution of social assistance often faces obstacles due to limited human resources and a manual selection process for recipients. Therefore, a Decision Support System (DSS) is needed to assist the selection process in a more objective and efficient manner. This study aims to develop a DSS for determining social assistance recipients in Manyaran Sub-district by combining the Analytic Hierarchy Process (AHP) and Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) methods. AHP is utilized to determine the weight of each criterion, while MOORA is used to calculate the final score of each recipient candidate. The results show that among the ten analyzed candidates, the individual coded P09 achieved the highest final score of 0.575. The top five candidates with the highest scores were declared eligible to receive social assistance, while the others were declared ineligible. The application of the AHP and MOORA methods in this DSS effectively improves the accuracy, objectivity, and efficiency of the selection process for social assistance recipients in Manyaran Sub-district.  
Co-Authors Affi Himmawan Agusta Praba Ristadi Pinem Agusta Praba Ristadi Pinem Akmal Rizqan Rafliansyah Alauddin Maulana Hirzan Angela Baptista Bernadine Frederica Annisa Aurelia Fitriani Aria Hendrawan, Aria Astrid Novita Putri, Astrid Atmoko Nugroho Basworo Ardi Pramono Bayu Adi Dewi Nurdiyah Fajriannoor Fanani Fitrianti, Ayang Hafidz Mufrodi S.Kom. Odi Handayani, Sri Henri Septa Maulana Indah Noor Lita Sari Ira Nurul Febriani Isa Ghani Al-Hadid Ismi Mufrodah Kadisi, Angelo Risang Khoirudin Khoirudin Kusuma Putra, Rafli Danu Kusumaningrum, Yulinda Laelasari, Iseu M. Zakki Abdillah Maftucha, Nidaul Miftakhur Rahman Miftakhur Rahman Muhajannah, Syarifatul Muhammad Arham Lutfi Muhammad Basyier Ardima Nabilah Putri Wijaya Nurtriana Hidayati Nurul Siti Mukaromah Nutriana Hidayati Palupi, Kinanthi Sekar Prind Triajeng Pungkasanti, Prind Triajeng RAFIKA HANJAYA PUTRI, LIEM, ANGELITA Rahmayuna, Novita Riki Ardi Pranata Rizal Rafli Fajar Kurniawan Rizqy Dhafin Son Hajee Rochmah, Shafira Nur Sa'adah, Annisa Lailis Salma, Saffanah Sekar Ayu Rahmawati Setiawan Adi Nugroho Setyawan, Nanda Hendra Siti Asmiatun, Siti Susanto, Susanto Tan Bagas Endrihartono Tantisnio, Fanika Putri Tasya, Ghiffani Azzaman Titis Handayani Victor Gayuh Utomo Vinay Rishiwal Wicaksana, Dinar Anggit Yosua Danny Santana Zuli Chofifah