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All Journal Journal of Innovation and Future Technology (IFTECH) Jurnal Teknologi Informatika dan Komputer APPA : Jurnal Pengabdian Kepada Masyarakat Journal of Informatics and Vocational Education
Tukiyat
Universitas Pamulang
Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Medicine & Pharmacology Social Sciences

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ANALISIS DAN EVALUASI KINERJA CHATBOT PENERIMAAN MAHASISWA BARU BERBASIS LLM DENGAN PENDEKATAN RAG Daerobby; Tukiyat; Ahmad Musyafa
Journal of Innovation And Future Technology Vol. 8 No. 1 (2026): Vol 8 No 1 (Februari 2026): Journal of Innovation and Future Technology (IFTECH
Publisher : LPPM Unbaja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/iftech.v8i1.4452

Abstract

The advancement of artificial intelligence has driven LLM-based chatbot implementation in education, particularly for New Student Admission (PMB) services. This research analyzes and evaluates an LLM-based PMB chatbot using Retrieval-Augmented Generation (RAG) at Politeknik Krakatau. The system integrates three LLM models (GPT-4 Turbo, Mistral Devstral, Xiaomi Mimo-v2) with FAISS and LangChain. Comprehensive evaluation uses eight standard metrics: four for retrieval (Recall@3, Precision@3, MRR, NDCG@3) and four for generation (BERTScore, ROUGE-1, ROUGE-L, METEOR), with 100 questions across six categories. Results show the retrieval component achieves excellent performance with Recall@3 of 1.000 (perfect), MRR of 0.756, and NDCG@3 of 0.864, indicating effective document finding and ranking. For generation, Mistral Devstral demonstrates best performance with BERTScore of 0.755, ROUGE-1 of 0.604, and METEOR of 0.427, followed by GPT-4 Turbo (BERTScore 0.723) and Xiaomi Mimo-v2 (BERTScore 0.718). These comprehensive results enable evidence-based model selection, producing a chatbot delivering accurate, contextually relevant, and consistent responses to prospective students. This directly addresses slow and inefficient admission services by reducing administrative workload through automated, high-quality information provision while improving response speed and reliability. Compared to previous studies, this research provides the most comprehensive evaluation of RAG-based PMB chatbots in Indonesia, with retrieval performance surpassing prior studies and offering actionable insights bridging technical metrics and real-world service improvement in higher education institutions.
Pemanfaatan Artificial Intelligence dalam Pengembangan Aplikasi Pembelajaran di Era Digital Mahardika Paramarta Laia; Ismatullah; Mizanul Hakim; Muhammad Haikal Abdussalam; Yudi Candra; Harasta Rahman Tri Putra; Tukiyat; Sajarwo Anggai
APPA : Jurnal Pengabdian Kepada Masyarakat Vol 3 No 4 (2025): APPA : Jurnal Pengabdian kepada Masyarakat
Publisher : Shofanah Media Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Perkembangan teknologi kecerdasan buatan (Artificial Intelligence/AI) telah membawa transformasi signifikan dalam bidang pendidikan, khususnya dalam pengembangan aplikasi pembelajaran digital. Penelitian ini menganalisis pemanfaatan AI dalam meningkatkan efektivitas pembelajaran melalui personalisasi konten, analitik prediktif, dan umpan balik real-time, dengan validasi melalui studi kasus implementasi di SMA Muhammadiyah 08 Ciputat. Dengan menggunakan metodologi studi literatur dan evaluasi empiris, kami mengidentifikasi tiga tema utama: (1) peluang pedagogis melalui pembelajaran adaptif dan penilaian otomatis, (2) peningkatan engagement siswa hingga 60% melalui chatbot dan tutor virtual, dan (3) tantangan etika terkait privasi data dan bias algoritma. Hasil implementasi praktis menunjukkan bahwa penerapan aplikasi pembelajaran berbasis AI meningkatkan pemahaman siswa sebesar 40-50% pada kelompok pilot, meningkatkan motivasi belajar secara signifikan, dan membantu guru dalam memantau perkembangan belajar siswa secara sistematis. Evaluasi terhadap 20 siswa kelas X menunjukkan peningkatan keaktifan belajar, pemahaman konsep yang lebih baik, dan hasil posttest yang lebih tinggi dibandingkan dengan pembelajaran konvensional. Namun, diperlukan kerangka kerja etika yang kuat, pelatihan guru yang komprehensif, dan pendampingan berkelanjutan untuk memaksimalkan manfaat AI dalam pendidikan digital. Penelitian ini menyimpulkan bahwa AI memiliki potensi besar untuk menjadi katalis transformasi pendidikan di era digital, tetapi dengan tetap mempertahankan peran humanistik pendidik dan komitmen terhadap pembelajaran yang inklusif.
Performance Evaluation of ARIMA, LSTM, and Hybrid ARIMA–LSTM Models for Daily Solar Energy Prediction in Bali Aslimah; Sajarwo Anggai; Tukiyat
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3283

Abstract

Solar energy is one of the most promising renewable energy sources in Indonesia, particularly in Bali, which has relatively high solar irradiance throughout the year. However, daily variability in solar radiation caused by weather conditions and atmospheric factors leads to fluctuations in solar energy production, making accurate forecasting essential for effective energy planning. This study aims to evaluate the performance of the Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and hybrid ARIMA–LSTM models in forecasting daily solar energy at the Jembrana Climatological Station, Bali. The dataset consists of 10-minute solar radiation observations obtained from an Automatic Weather Station (AWS) for the period January 2023 to September 2025, which were aggregated into daily solar energy values expressed in kWh/m². Data preprocessing included missing value handling, outlier correction, normalization, and an 80:20 split between training and testing datasets. Model performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The results show that the hybrid ARIMA–LSTM model achieved the best performance, with an RMSE of 0.960 kWh/m², MAE of 0.771 kWh/m², and MAPE of 22.245%, outperforming both the ARIMA and LSTM models. These findings indicate that the hybrid approach is more effective in capturing both linear and nonlinear characteristics of daily solar energy time series.
Predicting Solar Radiation Using Temporal Convolutional Networks and Long Short-Term Memory: A Comparative Study Based on Automatic Weather Station Data Famiyana Dewi; Tukiyat; Yan Mitha Djaksana
Journal of Informatics and Vocational Education Vol. 9 No. 3 (2026): November 2026
Publisher : Informatics Education Department, Faculty of Teacher Training and Education, Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/joive.v9i3.3758

Abstract

Solar radiation is a key meteorological parameter for climatology, hydrology, agriculture, renewable energy, and environmental analysis. Direct measurement remains limited because it requires specialised instruments, regular calibration, and high maintenance costs. This study compared Temporal Convolutional Network (TCN) and Long Short-Term Memory (LSTM) models for predicting solar radiation using Automatic Weather Station data from the Aceh Climatological Station. The dataset comprised 4,152 observations from January to December 2023, with rainfall, air temperature, relative humidity, air pressure, wind speed, wind direction, and time features as predictors, while solar radiation served as the target variable. The research process included data quality control, interpolation of invalid values, Min-Max Scaling, time feature engineering, sliding-window sequence generation with 24 time steps, and time-based data splitting using 70:15:15, 80:10:10, and 70:10:20 scenarios. Model performance was evaluated using MAE, RMSE, MAPE, and R². The 80:10:10 split produced the best results for both models. The LSTM model achieved slightly better overall performance than TCN based on RMSE and R², although both models captured the temporal pattern of solar radiation. The study provides an AWS-based temporal deep learning framework for radiation prediction in tropical regions of Indonesia.
Co-Authors Ahmad Musyafa Anggai, Sajarwo Aslimah Daerobby Famiyana Dewi Harasta Rahman Tri Putra Ismatullah Mahardika Paramarta Laia Mizanul Hakim Muhammad Haikal Abdussalam Yan Mitha Djaksana Yudi Candra