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All Journal JUITA : Jurnal Informatika RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Minfo Polgan (JMP)
Dziky Ridhwanullah
Universitas Tiga Serangkai
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Library & Information Science Mathematics Social Sciences

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PENGEMBANGAN SAVORYAI: SISTEM REKOMENDASI RESEP MASAKAN BERBASIS BAHAN DAN PREFERENSI KALORI MENGGUNAKAN CONTENT-BASED FILTERING DAN OPENAI API Ahmad Irfan Faiz; Dziky Ridhwanullah
Jurnal Teknologi Informasi dan Komunikasi (TIKomSiN) Vol 13, No 2 (2025): Jurnal Tikomsin, Vol 13, No.2, Oktober 2025
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/tikomsin.v13i2.1023

Abstract

The high volume of leftover cooking ingredients in households often leads to food waste due to a lack of ideas or references for processing them. This research aims to develop SavoryAI, an intelligent web-based recipe recommendation system that suggests relevan Indonesian dishes based on user-inputted ingredients and calorie preferences. The system integrates Content-Based Filtering (CBF) with cosine similarity and TF-IDF weighting to match user-selected ingredients with recipes in the database. Additionally, OpenAI’s GPT-4o model is utilized to identify food ingredients from uploaded images. The system is implemented using Laravel, Livewire, and TailwindCSS, with data gathered through interviews with household actors and literature reviews. Evaluation was conducted through functional testing (black-box), validity testing, and confusion matrix analysis, using response from household users to determine ground truth. The results show a high accuracy in generating relevant recipe recommendations, with a precision of 1.00, recall of 0.83, and F1-score of 0.91. The results show a high accuracy in generating relevant recipe recommendations. The integration of AI image recognition further enhances usability by enabling automatic ingredient input. The finding highlight the system’s effectiveness in reducing food waste and supporting sustainable cooking practices through personalized recipe suggestions.
Prediksi Kelulusan Siswa Sekolah Menengah Menggunakan XGBoost dan Analisis Feature Importance Luthfia Nurma Hapsari; Dziky Ridhwanullah; Miftakhurrokhmat Miftakhurrokhmat
Jurnal Minfo Polgan Vol. 15 No. 2 (2026): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v15i2.15782

Abstract

Prediksi performa akademik (student performance) siswa sekolah menengah merupakan bagian penting dalam kajian educational data mining untuk mendukung pengambilan keputusan pendidikan dan perancangan intervensi dini bagi siswa berisiko gagal. Pendekatan machine learning telah banyak digunakan untuk prediksi kelulusan siswa, namun sebagian besar penelitian masih berfokus pada akurasi model dan kurang menekankan aspek interpretabilitas hasil prediksi. Padahal, pemahaman faktor-faktor yang memengaruhi kelulusan sangat dibutuhkan oleh praktisi pendidikan. Penelitian ini bertujuan membangun model prediksi kelulusan siswa sekolah menengah menggunakan algoritma XGBoost serta mengidentifikasi fitur-fitur paling berpengaruh melalui analisis feature importance sebagai dasar rekomendasi intervensi pendidikan. Metode penelitian ini berada dalam konteks educational data mining dengan memanfaatkan dataset Student Performance dari UCI Machine Learning Repository yang terdiri dari 395 siswa dan mencakup variabel akademik, sosial, serta demografis. Data diproses melalui encoding fitur kategorikal dan pembagian data secara stratified dengan rasio 80:20. Model XGBoost dilatih dan dievaluasi menggunakan metrik accuracy, precision, recall, dan F1-score, dengan perhatian khusus pada kelas minoritas siswa berisiko gagal. Hasil evaluasi menunjukkan bahwa model mencapai accuracy sebesar 0,71 dan Macro F1-score sebesar 0,63. Analisis feature importance mengidentifikasi jumlah kegagalan sebelumnya, status wali, dukungan pendidikan tambahan, dan jumlah ketidakhadiran sebagai faktor paling berpengaruh terhadap prediksi kelulusan siswa. Kesimpulan yang diperoleh model XGBoost mampu memberikan prediksi kelulusan siswa yang memadai dalam analisis student performance, sekaligus menyediakan interpretasi fitur yang informatif. Integrasi pendekatan educational data mining dengan analisis feature importance menjadikan model ini aplikatif sebagai alat bantu pengambilan keputusan dan perancangan intervensi pendidikan berbasis data.
SENTIMENT ANALYSIS OF MOSQUITO-BORNE DISEASES ON SOCIAL MEDIA BASED ON BERT Dziky Ridhwanullah; Dwi Remawati; Ahmad Muhariya
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7733

Abstract

Mosquito-borne diseases, including malaria, dengue hemorrhagic fever (DHF), and chikungunya, remain significant public health challenges in Indonesia. With the extensive use of the internet, social media platforms such as X (Twitter) and Instagram have emerged as vital sources for capturing public sentiment in real-time. This study aims to identify and analyze Indonesian public sentiment regarding these diseases using a Deep Learning approach. The dataset comprises 1,800 records collected between 2024 and 2025 via web scraping. The methodology utilizes the BERT (Bidirectional Encoder Representations from Transformers) model for contextual feature extraction, evaluated through three Recurrent Neural Network (RNN) architectures: LSTM, GRU, and BiLSTM. The results reveal a predominant negative sentiment (56.99%), followed by neutral (32.99%) and positive (10.01%). While all models achieved high training accuracy (approx. 96%), the Gated Recurrent Unit (GRU) demonstrated superior generalization with the highest average validation accuracy of 94.24%. This study concludes that the GRU model is the most stable and effective architecture for classifying Indonesian public health sentiments on social media.
Evaluasi Pengaruh Sentimen Berita terhadap Pergerakan Harga Minyak Mentah dengan Pendekatan Klasifikasi Ahmad Muhariya; Indrawan Ady Saputro; Dziky Ridhwanullah
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.28411

Abstract

Various factors, including market perception reflected in media information, Influence crude oil price fluctuations. This study aims to analyse the Influence of news sentiment on crude oil price movements using a deep learning–based sentiment analysis approach. The dataset consists of 108 news headlines and daily closing oil prices from January to May 2025. It is important to note that this dataset is relatively small for deep learning models like LSTM, GRU, and BiLSTM, which constitutes a major constraint for this study. The news text was processed with case folding, tokenisation, stopword removal, and lemmatisation (not stemming to preserve semantic integrity for BERT), then automatically labelled using the DistilBERT model. The BERT-based vector representations were used as input for three classification models: LSTM, GRU, and BiLSTM. The evaluation results showed that all three models achieved the same average validation accuracy of 85.27%. However, the GRU model is identified as the optimal performer, achieving the lowest validation loss (0.3324), indicating better generalisation performance than LSTM and BiLSTM. Further analysis reveals that news sentiment tends to align with oil price trends, particularly during significant market shifts.
Otomasi Nutrisi Hidroponik Berbasis IoT untuk Greenhouse Mitra Soloraya melalui PjBL-OJT Ahmad Muhariya; Dziky Ridhwanullah; Yenny Rahmawati; Wawan Laksito Yuly Saptomo; Sapto Nugroho; Teguh Susyanto; Muhammad Hasbi; Saly Kurnia Octaviani; Ilham Fannani
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 7 No. 2 (2026): Edisi Mei - Agustus
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jpkmn.v7i2.9243

Abstract

Kesenjangan antara penguasaan teori Internet of Things (IoT) dan implementasi perangkat keras di lapangan menjadi tantangan pendidikan tinggi dalam mencetak talenta pertanian modern. Merespons hal ini, Program Studi S1 Informatika Universitas Tiga Serangkai (UTS) menyelenggarakan Pelatihan dan On-the-Job Training (OJT) "Smart Farming Project Based Learning" berkolaborasi dengan Edutic dan Balai Pelatihan Vokasi dan Produktivitas (BPVP) Surakarta. Kegiatan ini bertujuan menyelesaikan kendala pencampuran nutrisi hidroponik manual yang rentan tidak presisi pada unit usaha mitra. Melalui pendekatan Project Based Learning (PjBL), 16 mahasiswa lintas program studi dilibatkan mulai dari perancangan hingga implementasi purwarupa Smart Nutrition System pada empat greenhouse mitra. Hasil kegiatan menunjukkan 100% peserta dinyatakan kompeten pada Uji Kompetensi (UJK) skema otomasi nutrisi, dan tiga dari empat purwarupa sistem berhasil diimplementasikan secara berkelanjutan di lokasi mitra. Kolaborasi antara kampus, industri, dan lembaga vokasi terbukti efektif mencetak talenta digital sekaligus mengakselerasi digitalisasi sistem hidroponik pada level UMKM/BUMDes.
Co-Authors Ahmad Irfan Faiz Ahmad Muhariya Ahmad Muhariya Dwi Remawati Ilham Fannani Indrawan Ady Saputro Luthfia Nurma Hapsari Miftakhurrokhmat Miftakhurrokhmat Muhammad Hasbi Saly Kurnia Octaviani Sapto Nugroho Teguh Susyanto Wawan Laksito Yuly Saptomo Yenny Rahmawati