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All Journal Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jurnal Sains dan Teknologi Jurnal Ilmiah Informatika dan Ilmu Komputer
Muhammad Surono
Universitas Teknokrat Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Veterinary

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Peran Big Data dalam Inovasi Bisnis Digital: Pendekatan Tinjauan Literatur Sistematis Muhammad Fadli; Mugi Prasetio; Ival Sanjaya; Muhammad Surono; Mahendra Dewantoro; Ryan Randy Suryono
Jurnal Ilmiah Informatika dan Ilmu Komputer (JIMA-ILKOM) Vol. 4 No. 1 (2025): Volume 4 Nomor 1 March 2025
Publisher : PT. SNN MEDIA TECH PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jima-ilkom.v4i1.48

Abstract

Penelitian ini meninjau bagaimana Big Data berperan dalam inovasi bisnis digital, terutama dalam membantu pengambilan keputusan strategis, memperbaiki efisiensi operasional, serta menciptakan Produk dan layanan digital yang benar-benar pas dengan kebutuhan pengguna. Dengan menggunakan pendekatan tinjauan literatur sistematis, penelitian ini mengidentifikasi manfaat signifikan Big Data, termasuk kemampuannya untuk menyediakan analisis mendalam, memprediksi tren pasar, dan personalisasi layanan pelanggan. Namun, penelitian ini juga mengungkap berbagai tantangan dan kendala dalam implementasi Big Data, seperti keterbatasan infrastruktur teknologi, kualitas data yang rendah, serta isu privasi dan keamanan data. Hasil penelitian menunjukkan bahwa pemanfaatan Big Data yang optimal dapat meningkatkan daya saing bisnis digital, tetapi membutuhkan dukungan infrastruktur yang memadai dan kepatuhan terhadap regulasi yang berlaku. Studi ini berkontribusi pada pengembangan pemahaman tentang bagaimana Big Data dapat diintegrasikan ke dalam strategi inovasi bisnis digital untuk mendorong pertumbuhan dan keberlanjutan bisnis di era digital.
Analisis Sentimen Isu Redominasi Rupiah Menggunakan Lexicon Based dan Naïve Bayes Muhammad Fadli; Ival Sanjaya; Muhammad Surono; Ryan Randy Suryono
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 15, No 2 (2026): April 2026
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v15i2.3436

Abstract

Rupiah redenomination has resurfaced as a strategic issue in Indonesian monetary policy following the announcement of the roadmap by Minister of Finance Purbaya Yudhi Sadewa in 2025. This study aims to analyze public sentiment towards the redenomination issue through a hybrid approach: lexicon based (LBA) based on InSet Lexicon enriched with 217 contextual phrases (“bunker money”, “DPR ketar-ketir”), combined with Naive Bayes based on TF-IDF representation. The primary dataset of 1,087 public comments from YouTube (Isu_Ekonomi_Redominasi.csv) was processed using Sastrawi (stopword removal and stemming). The results show a dominance of positive sentiment (58.3%), driven by the justice frame narrative (redenomination as a tool for exposing corrupt assets), while negative (24.1%) and neutral (17.6%) sentiment reflect concerns over inflation risks and transition confusion. The MNB model achieved an average accuracy of 86.3% in 10-fold cross-validation. The findings reveal that public support is not merely monetary, but rather an expression of collective aspirations for state transparency and accountability. This research demonstrates the effectiveness of a hybrid lexicon-enhanced approach for domain-specific sentiment analysis in Indonesian, while also providing evidence-based policy insights for inclusive policy design.Keywords: Public Sentiment; Twitter Media; Naive Bayes; VisualizationAbstrakRedenominasi rupiah kembali mencuat sebagai isu strategis dalam kebijakan moneter Indonesia pasca pengumuman roadmap oleh Menteri Keuangan Purbaya Yudhi Sadewa pada 2025. Penelitian ini bertujuan menganalisis sentimen masyarakat terhadap isu redenominasi melalui pendekatan lexicon based berbasis InSet Lexicon yang diperkaya dengan 217 frasa kontekstual (misal: “uang bunker”, “DPR ketar-ketir”), dikombinasikan dengan Naive Bayes berbasis representasi TF-IDF. Dataset primer berupa 1.087 komentar publik dari YouTube (Isu_Ekonomi_Redominasi.csv) diproses menggunakan Sastrawi (stopword removal dan stemming). Hasil menunjukkan dominasi sentimen positif (58,3%), didorong oleh narasi justice frame (redenominasi sebagai alat eksposur aset korupsi), sementara sentimen negatif (24,1%) dan netral (17,6%) mencerminkan kekhawatiran atas risiko inflasi dan kebingungan transisi. Model Naive Bayes mencapai akurasi rata-rata 86,3% dalam 10 fold cross validation. Temuan mengungkap bahwa dukungan publik tidak hanya bersifat teknis moneter, melainkan ekspresi aspirasi kolektif terhadap transparansi dan akuntabilitas negara. Penelitian ini membuktikan bahwa pendekatan lexicon efektif untuk analisis sentimen domain spesifik dalam Bahasa Indonesia, sekaligus memberikan policy insight berbasis bukti untuk desain kebijakan inklusif. 
Hybrid XGBoost-SVM Model untuk Sistem Pendukung Keputusan dalam Prediksi Penyakit Diabetes Muhammad Surono; Muhammad Fadli; Dian Sri Purwanti; Erliyan Redy Susanto
INSOLOGI: Jurnal Sains dan Teknologi Vol. 4 No. 3 (2025): Juni 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v4i3.5410

Abstract

Diabetes is a chronic disease that continues to rise globally each year, requiring early detection for more effective prevention. This study develops an artificial intelligence-based decision support system for diabetes prediction using a Hybrid XGBoost-SVM model. The model combines the Support Vector Machine (SVM), known for its interpretability, with XGBoost (XGB), which enhances accuracy through boosting techniques. The study utilizes the Pima Indians Diabetes Dataset, undergoing preprocessing, normalization, data splitting, and model training. The evaluation compares accuracy, precision, recall, and F1-score across the three models. Experimental results indicate that XGBoost and SVM both achieve an accuracy of 75%. However, the Hybrid XGBoost-SVM model provides consistently improved performance, achieving the highest accuracy (77%), along with increased precision (70%) and F1-score (65%). Although the numerical improvement in accuracy appears relatively small, this enhancement is significant in the medical context, especially due to improved precision and balanced classification. This study concludes that the Hybrid XGBoost-SVM approach offers a more optimal and reliable alternative in decision support systems for diabetes prediction. Future research can explore other model combinations, such as Stacking or Weighted Voting, to enhance predictive performance further.
Co-Authors Dian Sri Purwanti Erliyan Redy Susanto Fadli, Muhammad Ival Sanjaya Mahendra Dewantoro Mugi Prasetio Muhammad Fadli Muhammad Fadli Ryan Randy Suryono