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PENERAPAN TEOREMA BAYES PADA SISTEM PAKAR DIAGNOSA GASTROINTESTINAL Wahyuni, Suci; Wiyandra, Yogi; Zain, Ruri Hartika; Kurnia, Hezy; Yenila, Firna
Journal of Information System Management (JOISM) Vol. 5 No. 2 (2024): Januari
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/joism.2024v5i2.1396

Abstract

Gastrointestinal merupakan penyakit yang disebabkan oleh permaslaahan pada bagian pencernaan yang memiliki fungsi yang tidak maksimal. Hal tersebut terjadi disebabkan karena proses penyerapan makanan dan nutrisi menjadi tidak seimbang. Sistem gastrointestinal melibatkan semua organ dalam dari mulut sampai anus. Pentingnya pemahaman tentang permasalahan gastrointestinal perlu disosialisasikan untuk memberikan edukasi kepada Masyarakat mengenai kondisi tersebut. Salah satu alasan dalam melakukan penelitian ini adalah memberikan informasi berbasis pengetahuan melalui aplikasi yang disampaikan oleh pakar dalam memberikan edukasi kepada Masyarakat mengenai gastrointestinal. Penelitian ini dilakukan dengan menggunakan aplikasi berbasis online berupa sistem pakar dengan mengusung metode teorema bayes yang mampu menghubungkan tingkat keyakinan user (prior) kepada keyakinan baru (posterior) setelah adanya suatu observasi baru (evidence) berdasarkan kemungkinan tertentu. Hasil penelitian ini terhadap ujicoba salah satu rule yang diberikan memberikan nilai keyakinan 32.04% sehingga pengujian tersebut memberikan nilai sesuai dengan ketentuan yang telah ditetapkan oleh pakar.
Robust Predictive Model for Heart Disease Diagnosis Using Advanced Machine Learning Techniques Sovia, Rini; Anam, M. Khairul; Wisky, Irzal Arief; Permana, Randy; Rahmi, Nadya Alinda; Zain, Ruri Hartika
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1092

Abstract

This study presents a hybrid ensemble learning framework designed to enhance the predictive accuracy, robustness, and generalizability of heart disease classification models. The framework integrates three base classifiers: Decision Tree (DT), Gaussian Naive Bayes (GNB), and K Nearest Neighbor (KNN), which are combined using a stacking ensemble method with Logistic Regression (LR) as the meta learner. Each classifier contributes a distinct analytical perspective: DT models nonlinear relationships, GNB provides probabilistic reasoning, and KNN captures similarity-based patterns. Logistic Regression aggregates their outputs to produce a unified predictive decision. To mitigate class imbalance commonly observed in clinical datasets, the Synthetic Minority Oversampling Technique (SMOTE) is applied to generate synthetic samples of the minority class, improving the model’s ability to recognize underrepresented cases. Hyperparameter optimization is performed using the Optuna framework, which applies the algorithm to efficiently explore parameter configurations. The proposed model was evaluated on a publicly available heart disease dataset and achieved an accuracy of 99.61%, precision of 99.62%, recall of 99.59%, F1 score of 99.60%, and specificity of 99.58%, corresponding to a false positive rate of only 0.42 percent. These results demonstrate the framework’s strong ability to accurately identify heart disease cases while minimizing misclassification. The integration of SMOTE, stacking, and Optuna optimization contributes to its superior performance and robustness. Consequently, this approach shows strong potential for integration into clinical decision support systems to assist healthcare professionals in reliable and timely diagnosis.
PEMBERDAYAAN SISWA SMK PERHOTELAN DALAM SPEAKING BAHASA INGGRIS FRONT OFFICERS MENGGUNAKAN TEKNOLOGI APLIKASI BERBASIS PYTHON Christina, Dian; Zain, Ruri Hartika; Adha, Annisha Dyuli
JMM (Jurnal Masyarakat Mandiri) Vol 9, No 6 (2025): Desember
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jmm.v9i6.35626

Abstract

Abstrak: Keterampilan berbicara bahasa Ing,gris merupakan kemampuan penting bagi siswa SMK Perhotelan, terutama bagi mereka yang akan bekerja di bidang front office. Namun, pembelajaran di sekolah sering kali masih berfokus pada teori dan kurang memberikan kesempatan praktik berbicara secara aktif. Kegiatan pengabdian kepada masyarakat (PKM) ini bertujuan untuk memberdayakan siswa melalui pelatihan speaking berbasis aplikasi Python yang dirancang untuk melatih percakapan interaktif dalam konteks pelayanan hotel. Fokus dari pelatihan ini untuk meningkatkan soft skill siswa, terutama dalam hal komunikasi dan kepercayaan diri dalam berbahasa inggris. Kegiatan ini melibatkan 34 siswa jurusan Perhotelan yang mengikuti pre-test dan post-test, dengan 20 soal yang digunakan untuk mengukur peningkatan kemampuan berbicara mereka. Hasil analisis data menggunakan uji Wilcoxon menunjukkan nilai signifikansi < 0,001, yang berarti terdapat peningkatan kemampuan berbicara secara signifikan setelah pelatihan. Berdasarkan hasil posttest, kompetensi speaking siswa meningkat sebesar 17,5%. Hasil ini membuktikan bahwa penerapan teknologi berbasis Python efektif dalam meningkatkan kompetensi komunikasi siswa, serta relevan dengan kebutuhan dunia industri perhotelan.Abstract: English speaking skills are crucial for vocational high school students majoring in Hospitality, especially for those who will work in the front office. However, learning in schools often still focuses on theory and lacks opportunities for active speaking practice. This community service activity (PKM) aims to empower students through Python-based speaking training designed to practice interactive conversations in the context of hotel services. The focus of this training is to improve students' soft skills, particularly in terms of communication and confidence in speaking English. This activity involved 34 students majoring in Hospitality who took pre-tests and post-tests with 20 questions used to measure improvements in speaking skills. Data analysis using the Wilcoxon test showed a significance value of < 0.001, indicating a significant improvement in speaking skills after training. Based on the post-test results, students' speaking competency increased by 17.5%. These results prove that the application of Python-based technology is effective in improving students' communication skills and is relevant to the needs of the hospitality industry.
Rancang Bangun Alat Ukur Kadar Protein Pada Makanan Pokok Berbasis Iot Dengan Kendali BOT Telegram Billy Hendrik; Ruri Hartika Zain; Afifah Syahidah Nahda
Jurnal Ilmu Komputer dan Informatika | E-ISSN : 3063-9026 Vol. 2 No. 4 (2026): April - Juni
Publisher : GLOBAL SCIENTS PUBLISHER

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

Abstract

Protein is one of the macronutrients that is very important and essential for the human body. This substance functions in various biological processes, including as a builder of muscle tissue, skin, enzymes, hormones, and the body's immune system. Although important, monitoring daily protein consumption is often not carried out accurately by the general public. Most people only rely on rough estimates based on the type of food consumed, or nutritional information printed on the packaging label. This is a challenge, especially for staple foods or home-made foods that do not have nutritional labels. One technology that supports the development of this system is a load cell, which is able to measure the mass or weight of an object precisely and stably. This sensor is widely used in digital scales and industrial systems. In the context of protein measurement, the weight of a food ingredient can be converted to an estimate of its protein constent, based on the average protein content data of each type of food that has been determined. With the help of a microcontroller such as Arduino, ESP32, or other types, this system can regulate the logic flow of the tool's operation, calculate protein levels based on input from the load sensor, and display the measurement results to the user via an output device such as a 20x4 LCD. In addition, the use of infrared (IR) sensors as object presence detectors allows the system to recognize when food has been placed on the device, so that the measurement process can be carried out automatically and responsively.
Language Processing for Detecting Fake News on Twitter Using a Long Short-Term Memory Architecture Rini Sovia; Dwi Andhara Valkyrie; Ruri Hartika Zain; Firdaus
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6570

Abstract

The rapid spread of misinformation on social media platforms, particularly X (formerly Twitter), poses a significant challenge to public trust and democratic integrity. Fake news is often crafted to deceive readers and manipulate public opinion, especially in political contexts such as the 2024 Regional Head Elections (Pilkada 2024). Although various measures have been proposed to mitigate this issue, achieving an effective balance between controlling misinformation and preserving free speech remains a challenge. This study aims to address this problem by developing a fake news detection model based on Natural Language Processing (NLP) and Long Short-Term Memory (LSTM). The dataset used in this study was collected from public tweets related to Pilkada, with Kompas.com serving as the validation source to verify content authenticity. Experimental results show that the proposed LSTM model outperformed traditional classification methods, achieving a precision, recall, and F1-score of 0.95, along with an overall accuracy of 94.90%. Confusion matrix analysis further confirmed the reliability of the model by demonstrating low misclassification rates. This study contributes to the advancement of AI-driven hoax detection systems, offering an automated and scalable solution for combating misinformation in political discourse.
PELATIHAN PEMANFAATAN TEKNOLOGI DIGITAL UNTUK MENINGKATKAN KEAMANAN DAN PERTUMBUHAN UMKM DI ERA TRANSFORMASI DIGITAL Ruri Hartika Zain; Riandana Afira; Hasri Awal; Zulfitri Yani
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 6 No. 1 (2025): Volume 6 No. 1 Tahun 2025
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v6i1.41392

Abstract

UMKM memegang peranan penting dalam perekonomian Indonesia, namun banyak pelaku usaha menghadapi kesulitan dalam beradaptasi dengan kemajuan teknologi digital. Di era transformasi digital, pemanfaatan teknologi dapat meningkatkan efisiensi operasional, memperluas pasar, dan memperkuat keamanan data serta transaksi bisnis. Oleh karena itu, pelatihan tentang pemanfaatan teknologi digital sangat dibutuhkan untuk mendukung pertumbuhan dan keberlanjutan UMKM. Pengabdian masyarakat ini bertujuan untuk memberikan pelatihan kepada pelaku UMKM mengenai penggunaan teknologi digital yang tepat, khususnya dalam aspek keamanan digital dan pemasaran online. Metode yang digunakan dalam pengabdian ini adalah pelatihan langsung yang mencakup topik penggunaan aplikasi keamanan, platform e-commerce, serta teknik pemasaran digital. Hasil dari pelatihan menunjukkan adanya peningkatan pemahaman peserta dalam menggunakan teknologi digital secara aman dan efektif, yang berpengaruh positif terhadap pertumbuhan usaha dan pengelolaan data. Pelatihan ini terbukti penting untuk membantu UMKM agar dapat bersaing di pasar digital secara lebih aman dan berkembang.
Gerry Saputra Implementasi Radio Frequency Identification Dan Camera Pada Sistem Gerbang Masuk Dan Keluar Perbatasan Negara Berbasis Arduino Uno Dan Gui Gerry Saputra; Ruri Hartika Zain; Riska Robianto
Journal of Computer Scine and Information Technology Volume 9 Issue 3 (2023): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jcsitech.v9i3.145

Abstract

Currently, the activities of entering and leaving the country in Indonesia require a long period of time and incur significant costs in day-to-day operations. One of the reasons is the increasing population who wishes to travel in and out of the country, leading to long queues and consuming much time. Moreover, the process of identifying passport legality is still done manually, resulting in a time-consuming passport handling process. The purpose of this research is to develop a system that facilitates traveling in and out of the country and registers passport data for those who do not have an ID Tag, thereby reducing the duration of travel in and out of the country.
Sistem Keamanan Kotak Penyimpanan Barang Di Tempat Bermain Anak Dengan Pemanfaatan Qr-Code Dan Sensor Fingerprint Aditya Permana; Mardiah Masril; Ruri Hartika Zain
Journal of Computer Scine and Information Technology Volume 10 Issue 1 (2024): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jcsitech.v10i1.150

Abstract

Security system is a set of procedures, technologies, software, and hardware designed to protect an entity or environment from threats, attacks, or security breaches. The main purpose of a security system is to maintain the confidentiality, integrity, and availability of data or protected resources. In the context of the need for playgrounds that also serve as learning environments prioritizing safety and comfort, concerns arise regarding the security of personal belongings storage. When visiting playgrounds, visitors often bring valuable items such as mobile phones, wallets, or other electronic devices. The aim of this research is to create an effective and integrated security system to protect the valuable belongings of playground visitors. The method involves the design and implementation of a security system consisting of QR-Code as an identification mechanism and fingerprint sensors as an additional security layer. Data is collected through system testing to evaluate the performance and reliability of the proposed security system. The findings of this research are expected to contribute to improving the safety and comfort of playground visitors and serve as a reference for similar security system research and development in the future.
Perancangan Sistem Peminjaman Papan Surfing Menggunakan Rfid, Barcode Scanner Dan Delphi 7 Riska Amelia; Rini Sovia; Ruri Hartika Zain
Journal of Computer Scine and Information Technology Volume 10 Issue 1 (2024): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

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

Abstract

Water surfing has become a tourism industry worth billions, where millions of surfers travel around the world to several water surfing destinations in search of the 'perfect wave'. It is now estimated that there are more than 10 million water surfers in the world and this continues to increase at 12-16% per year. These surfers also visit places in the world that they feel are in harmony with what they are looking for. In one place they visited there were surfboard rental kiosks.Surfboard rental service sellers will serve visitors to rent their surfboards. Because there are too many surfboard enthusiasts, it is too difficult for surfboard rental service sellers to serve visitors. From these problems, a system is needed that can simplify the process and data collection of surfboard borrowing.
A mixed integer linear programming approach for last-mile e-commerce optimization through micro-fulfillment centers Fristi Riandari; Ruri Hartika Zain
Journal of Intelligent Decision Support System (IDSS) Vol 9 No 1 (2026): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v9i1.338

Abstract

The rapid growth of e-commerce increases the complexity of last-mile delivery due to high distribution costs, urban congestion, and increasingly tight delivery time demands. This study proposes a Mixed Integer Linear Programming (MILP) approach to optimize e-commerce last-mile distribution through the determination of Micro-Fulfillment Centers (MFCs). The model simultaneously determines (i) the locations of candidate MFCs to be opened and (ii) the allocation of demand zones to selected facilities, with the objective of minimizing the total network cost consisting of fixed facility costs and variable last-mile service costs. Service quality is enforced through a hard service level agreement (SLA) mechanism by limiting allocation to only pairs of facility zones that meet a certain travel time threshold, while operational feasibility is guaranteed through capacity constraints at each MFC. The model outputs are implementable in the form of selected MFC locations, zone allocation maps, and performance indicators for evaluation, including total cost decomposition, weighted travel time metrics, and facility capacity utilization to identify potential bottlenecks. Numerical illustrations show that the MILP formulation yields feasible location–allocation decisions with respect to SLA and capacity, while avoiding the “closest/fastest” heuristic that can potentially lead to facility overload. This framework supports decision-makers in designing efficient, responsive, and scalable last-mile networks, and can be extended to incorporate demand uncertainty, SLA penalties (soft-SLAs), multi-echelon structures, and sustainability objectives.