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Jauhar Vikri, Muhammad
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Pendeteksian Dini pada Potensi Banjir dengan Berbasis Internet of Things Menggunakan Algoritma Random Forest Ardyka Bayu Reovan; Guruh Putro Dirgantoro; Muhammad Jauhar Vikri
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 2 (2026): April 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i2.9644

Abstract

Flood is a natural disaster that frequently occurs and causes significant material and social losses. Conventional flood monitoring systems are generally reactive and rely on threshold-based approaches, which limits their effectiveness in supporting early detection. This study proposes an Internet of Things (IoT)-based early flood detection system using the Random Forest algorithm. The developed system collects hydrological data, including water level and flow velocity, through IoT sensors that periodically transmit data to a server for further processing. The collected data are then aggregated and classified into flood and non-flood conditions using a Random Forest model. Model performance is evaluated using accuracy, precision, recall, f1-score, confusion matrix, and 5-fold cross-validation. Experimental results indicate that the proposed model achieves an accuracy of 97.26% with a mean cross-validation score of 0.9863. However, the recall for flood events remains limited due to data imbalance and the relatively small number of flood occurrences. Despite these limitations, the proposed system demonstrates potential to support the development of early flood warning systems and can be further improved by incorporating longer and more diverse historical datasets.
Rice Quality Identification Built on Indonesian Food Standards Based on Electronic Nose using Naïve Bayes Algorithm Muhammad Jauhar Vikri; Ifnu Wisma Dwi Prastya; Ucta Pradema Sanjaya; Mula Agung Barata
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/0y0xct32

Abstract

Rice is a staple food in Indonesia, where its quality is regulated by the National Food Standards outlined in National Food Agency Regulation No. 2 of 2023 on Rice Quality and Labeling Requirements. Rice is classified into four grades: premium, medium 1, medium 2, and medium 3. The widespread practice of mislabeling lower-quality rice as a premium through repackaging highlights the critical need for quality control measures. An electronic nose (e-nose) is a reliable device for food quality control. Previous studies have demonstrated its ability to classify rice into two quality grades with 80% accuracy. This study uses exponential data transformation and the Naive Bayes algorithm to enhance the classification accuracy for four rice quality grades according to national standards. The methodology includes signal acquisition, feature extraction using statistical parameters, exponential data transformation, classification, and performance evaluation. The results show that exponential data transformation improves classification accuracy to 97%. This technology can be implemented for automated quality control in milling facilities, storage warehouses, and distribution centres, ensuring consistent rice quality while enhancing supply chain efficiency. The e-nose-based model offers a fast and reliable solution, minimising reliance on human operators.
Implementasi SMOTE dan GrideSearchCV untuk Klasifikasi Sentimen Imbalanced pada Isu Reshuffle Kabinet Muhamad Rofi'i; Muhammad Jauhar Vikri; Roihatur Rohmah
VOCATECH: Vocational Education and Technology Journal Vol 8, No 1 (2026): April
Publisher : Akademi Komunitas Negeri Aceh Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38038/vocatech.v8i1.305

Abstract

AbstractSocial media has become a rapid barometer of public response to national issues. On X (Twitter), users not only share information but also shape opinions, criticism, and support toward political events. Sentiment analysis is therefore essential for capturing public perceptions explicitly and tracing the dynamics of policy acceptance in digital spaces. This study evaluates the effectiveness of SMOTE and GridSearchCV in improving machine learning performance for imbalanced sentiment classification. It is an applied quantitative study with a comparative computational experimental design. The dataset comprises 6,115 Indonesian-language tweets about a cabinet reshuffle, with (83.5%) negative and 16.5% positive sentiment. Data are preprocessed, labeled using a lexicon-based dictionary, vectorized with TF-IDF, and split into training and test sets. Logistic Regression, Naïve Bayes, SVM, and Random Forest are compared under three scenarios: baseline (no SMOTE), SMOTE, and SMOTE with GridSearchCV for hyperparameter search. Macro F1 is the primary metric, supported by a confusion matrix for per-class evaluation, ensuring minority-class performance is not obscured by high aggregate accuracy. Results show the baseline achieves high accuracy (86.02–89.13%) but low positive recall (21.29–42.57%) and macro F1 (62.83–75.09%), indicating majority-class bias. With SMOTE, positive recall rises (58.42–66.34%) and macro F1 improves (71.50–79.11%) while accuracy remains relatively stable (81.77–88.39%). The best model is Logistic Regression with (88.23%) accuracy, (78.90%) macro F1, and (65.84%) positive recall. In conclusion, SMOTE reduces majority-class bias, GridSearchCV stabilizes performance gains, and macro F1 proves more representative than accuracy for imbalanced sentiment data. AbstrakMedia sosial kini menjadi barometer cepat respon publik terhadap isu nasional. Melalui X/Twitter, masyarakat bukan hanya berbagi informasi, tetapi juga membentuk opini, kritik, dan dukungan atas peristiwa politik. Karena itu, analisis sentimen penting untuk menangkap persepsi publik secara eksplisit dan membaca dinamika penerimaan kebijakan di ruang digital. Penelitian ini bertujuan mengevaluasi efektivitas SMOTE dan GridSearchCV dalam meningkatkan kinerja algoritma machine learning pada klasifikasi sentimen yang tidak seimbang. Studi ini menggunakan metode kuantitatif terapan dengan desain eksperimen komputasional komparatif. Dataset penelitian 6.115 tweet berbahasa Indonesia bertopik reshuffle kabinet dengan distribusi 83,5% negatif dan 16,5% positif. Data dipra-pemrosesan, dilabeli memakai kamus lexicon-based, direpresentasikan dengan TF–IDF, lalu dipisah menjadi data latih dan uji. Klasifikasi diterapkan menggunakan Logistic Regression, Naïve Bayes, SVM, dan Random Forest pada tiga skenario: baseline tanpa SMOTE, SMOTE dengan baseline, serta SMOTE+GridSearchCV untuk pencarian hyperparameter. Macro F1 ditetapkan sebagai metrik utama, didukung confusion matrix untuk evaluasi per kelas. Hasil menunjukkan baseline memberi akurasi tinggi (86,02–89,13%), tetapi recall positif rendah (21,29–42,57%) dan macro F1 (62,83–75,09%), menandakan bias kelas mayoritas. SMOTE meningkatkan recall positif (58,42–66,34%) dan macro F1 (71,50–79,11%) dengan akurasi relatif stabil (81,77–88,39%). Kinerja terbaik dicapai Logistic Regression dengan akurasi (88,23%), macro F1 (78,90%), dan recall positif (65,84%). Simpulan penelitian menunjukkan SMOTE efektif menekan bias kelas mayoritas dan GridSearchCV menstabilkan peningkatan kinerja. Macro F1 terbukti lebih representatif daripada akurasi untuk data sentimen timpang, sehingga pendekatan ini layak diterapkan pada kasus klasifikasi teks tidak seimbang lainnya.
SOSIALISASI PENERAPAN BERMEDIA INTERNET UNTUK ANAK USIA DINI PADA GURU DAN ORANG TUA DI RA AL FATHIMIYAH SUGIHWARAS BOJONEGORO Roihatur Rohmah; Muhammad Jauhar Vikri; Putri Liana
Al-Khidmat : Jurnal Ilmiah Pengabdian Kepada masyarakat Vol. 6 No. 1 (2023): Jurnal Al-Khidmat : Jurnal Ilmiah Pengabdian Kepada Masyarakat
Publisher : Pusat Pengabdian kepada Masyarakat LP2M UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/jak.v6i1.15769

Abstract

AbstrakPendidikan usia dini yang banyak diadakan oleh yayasan pendidikan mempunyai konsep yang berbeda-beda. Salah satu yayasan pendidikan anak yang ada di sugihwaras bojonegoro adalah RA Alfathimiyah. Anak-anak yang bersekolah disana dengan berbagai sifat yang berbeda-beda. Ada beberapa anak yang sifatnya dominan temperamen dan introvert. Setelah dikaji, anak-anak tersebut ketika di rumah terlalu banyak melakukan kegiatan screen time tanpa pengawasan yang tepat dari orang tua mereka. Dengan adanya permasalahan tersebut, maka diadakan sosialisasi kepada guru dan orang tua anak mengenai penerapan penggunaan media internet pada pembelajaran anak usia dini di RA Al fathimiyah sugihwaras. Kegiatan pengabdian kepada masyarakat ini bertujuan memberikan pengetahuan kepada guru untuk disosialisasikan kepada orang tua mengenai penerapan media internet yang tepat untuk anak usia dini. Metode yang digunakan yaitu sosialisasi dengan memberikan pre-test, presentasi, dan post-test kepada guru dan orang tua. Kegiatan sosialisasi telah dilakukan pada 18 Desember 2021 dengan peserta 30 orang. Dari hasil pre-test ditunjukkan bahwa orang tua memberikan gadget kepada anak 44% mulai usia 2 tahun, 55% mulai usia 3 tahun, dan 15 mulai usia 4 tahun. Sedangkan dari hasil post-test ditunjukkan bahwa 78% peserta sudah memahami penggunaan gadget dengan durasi waktu yang sesuai dengan usia anak dan semua peserta sudah melakukan pengalihan aktivitas untuk meminimalisir anak bermedia internet. AbstractEarly childhood education held by many educational foundations has different concepts. One of the children's education foundations in Sugihwaras Bojonegoro is RA Alfathimiyah. The children who go to school there are of many different natures. There are some children whose temperament is dominant and introverted. After studied, these children when at home do too much screen time without proper supervision from their parents. With these problems, socialization was held to teachers and parents of children regarding the application of the use of internet media in early childhood learning at RA Al Fathimiyah Sugihwaras. This community service activity aims to provide knowledge to teachers to be socialized to parents regarding the application of appropriate internet media for early childhood. The method used is socialization by giving pre-test, presentation, and post-test to teachers and parents. The socialization activity was carried out on December 18, 2021 with 30 participants. From the results of the pre-test, it was shown that parents gave gadgets to their children 44% from the age of 2 years, 55% from the age of 3 years, and 15 from the age of 4 years. Meanwhile, from the post-test results, it was shown that 78% of participants had understood the use of gadgets with a time duration that was appropriate to the child's age and all participants had diverted activities to minimize children using internet media.
Design and Development of an IoT-Based Vital Sign Monitoring System Using Mamdani Fuzzy Logic and Firebase Realtime Database Putri Liana; Muhammad Jauhar Vikri; Afril Efan Pajri
Journal of Applied Informatics and Computing Vol. 10 No. 5 (2026): October 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i5.13727

Abstract

Vital sign monitoring plays an important role in supporting early detection of health conditions. However, most Internet of Things (IoT)-based health monitoring systems are limited to displaying physiological data without providing automatic health status classification and integrated real-time cloud-based monitoring. This study aims to design and develop an IoT-based vital sign monitoring system using Mamdani Fuzzy Logic and Firebase Realtime Database to classify health conditions into Normal, Alert, and Emergency categories. The proposed system employs a NodeMCU ESP8266 microcontroller, a MAX30102 sensor to measure heart rate and blood oxygen saturation (SpO₂), an MLX90614 sensor to measure body temperature, Firebase Realtime Database for cloud data synchronization, and a web-based monitoring dashboard. The Mamdani Fuzzy method utilizes three input variables with 15 fuzzy rules, Max–Min inference, and centroid defuzzification. Sensor validation was conducted on five healthy adult respondents aged 25–30 years by comparing the sensor measurements with reference medical devices. The MLX90614 sensor achieved a Mean Absolute Error (MAE) of 0.136°C, a Root Mean Square Error (RMSE) of 0.140°C, and a correlation coefficient of 0.998. For heart rate measurement, the MAX30102 sensor produced an MAE of 1.6 BPM, an RMSE of 1.673 BPM, and a correlation coefficient of 0.956, while SpO₂ measurement resulted in an MAE of 1.2%, an RMSE of 1.265%, and a correlation coefficient of 0.968. Verification of the Mamdani Fuzzy implementation against MATLAB simulation showed an average difference of 0.37 on a fuzzy output scale of 0–100 without affecting the resulting health status classification. The developed system successfully performs real-time vital sign monitoring and automatic health status classification through a web interface and Firebase Realtime Database. Nevertheless, the system is intended as an initial health monitoring support tool rather than a medical diagnostic device, as clinical validation with healthcare professionals has not yet been conducted.
Co-Authors Abda Abda Abdul Faqih Achmad  Afandi Afril Efan Pajri Ahmad Sahru Romadhon Andhi Setiawan Aralia, Shafa Kirana Ardyka Bayu Reovan Aries Alfian Prasetyo Bakti Putra Pamungkas Barata, Mula Bassalam, Ahmad Nasih Ulwan Denny nur Diansyah Fidiya, Karisma Risma Fitri Damayanti Guruh Putro Dirgantoro Ifnu Wisma Dwi Prastya Indah Khoirun Nisa Ita Aristia Sa'ida Karisma Risma Fidiya Kartini, Alif Yuanita Laily Ulfiyah Moh. Muhajir Muarofah, Isna Ayu Muhamad Rofi'i Mula Agung Barata Nailus Saidah Anindia Septiana Nugraha, Krisna Wahyu Pradema Sanjaya, Ucta Putri Liana Rheyna Anggri Setyani Roihatur Rohmah Sunu Wahyudhi Teguh Pribadi Ucta Pradema Sanjaya Umar Farid Al Faqihi Vita Dwi Rahmawati Zakki Alawi