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All Journal CommIT (Communication & Information Technology) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jiko (Jurnal Informatika dan komputer) Jurnal Inovasi Pengabdian dan Pemberdayaan Masyarakat
Setyawan, Arif Fitra
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Pelatihan Mengintegrasikan IPCam ke Cloud untuk Manajemen Keamanan Kesehatan di Klinik Pratama Halyna Kebongembong Kecamatan Pagerruyung Kabupaten Kendal, Jawa Tengah Setyawan, Arif Fitra; Ariyanto , Amelia Devi Putri; Fikriah, Fari Katul
Jurnal Inovasi Pengabdian dan Pemberdayaan Masyarakat Vol 4 No 1 (2024): JIPPM - Juni 2024
Publisher : CV Firmos

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54082/jippm.416

Abstract

Kegiatan Pengabdian masyarakat ini bertujuan untuk meningkatkan manajemen keamanan kesehatan di Klinik Pratama Halyna, yang terletak di Kecamatan Pagerruyung, Kabupaten Kendal, Jawa Tengah, melalui pelatihan dalam mengintegrasikan IPCam ke sistem cloud. Klinik ini memiliki permasalahan dalam memonitor keamanan pasien dan lingkungan klinik secara efektif. Program pengabdian ini dirancang untuk melatih staf klinik dalam pemasangan, pengaturan dan penggunaan IPCam. Metodologi pelatihan menggunakan pendekatan partisipatif yang melibatkan kolaborasi antara tim pengabdian dan staf klinik. Pelatihan dilakukan dalam beberapa tahap, dimulai dengan pemahaman dasar tentang teknologi IPCam dan platform cloud, diikuti dengan pelatihan praktis tentang instalasi, konfigurasi, dan manajemen rekaman data. Evaluasi dilakukan melalui survei prapostes dan survei kepuasan peserta setelah pelatihan. Hasil dari program ini menunjukkan peningkatan yang signifikan dalam pemahaman dan keterampilan staf klinik dalam mengintegrasikan IPCam ke sistem cloud. Para peserta mempraktikkan secara langsung menggunakan teknologi tersebut untuk memantau dan mengelola keamanan klinik melalui smartphone. Selain itu, partisipasi dalam pelatihan juga meningkatkan kesadaran akan pentingnya keamanan data pasien dan kepatuhan terhadap regulasi kesehatan yang berlaku. Dampak jangka panjang dari program ini meliputi peningkatan keamanan klinik, peningkatan efisiensi operasional dan peningkatan pelayanan kesehatan kepada masyarakat. Program ini juga memberikan kontribusi positif terhadap pengembangan infrastruktur teknologi informasi di tingkat lokal.
KLASIFIKASI HASIL MRI TUMOR OTAK DENGAN EKTRAKSI FITUR GRAY LEVEL CO-OCCURANCE MATRIX (GLCM) Fikriah, Fari Katul; Ariyanto, Amelia Devi Putri; Setyawan, Arif Fitra
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 9 No 2 (2024): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v9i2.4793

Abstract

Bagian penting dari tubuh adalah otak yang mana menjadi sumber dari semua alat tubuh yang terletak dalam rongga tengkorak, tumor otak merupakan salah satu penyakit yang dapat menyerangnya. Pendeteksian tumor otak adalah salah satu aspek yang dinilai penting dalam diagnosa medis. Pada penelitian ini memiliki tujuan melakukan implementasi ekstraksi fitur GLCM (Gray Level Co-occurence Matrix) pada citra MRI tumor otak serta mencari performa algoritma yang paling baik dari deteksi tumor otak menggunakan citra MRI ini. Data yang dipakai pada penelitian ini merupakan data public yang berasal dari kaggle.com. Proses ekstraksi fitur pada citra digunakan pada penelitian ini GLCM yang mana memiliki fungsi menghitung frekuensi dari nilai intensitas piksel yang berjarak antar citra dengan menggunakan parameter 0o, 45o, 90o, 135o. Tahap selanjutnya pada penelitian ini adalah dengan melakukan langkah preprocessing dengan selanjutnya mencari nilai klasifikasi dari hasil MRI menggunakan algoritma Naïve Bayes, C4.5 dan Neural Network. Hasil yang didapatkan memperlihatkan bahwa Naïve Bayes memiliki performa algoritma paling baik dibandingkan C4.5 dan Neural Network yaitu dengan akurasi algoritma Naïve Bayes sebesar 96.8%, sedangkan untuk algoritma C4.5 sebesar 41.5% dan Neural Network sebesar 38.25%. selain hal tersebut pada penelitian ini membuktikan bahwa dengan ekstraksi fitur GLCM terbukti efektif dalam menangkap informasi tekstur dari citra MRI yang sangat penting pada klasifikasi tumor otak.
CLASSIFICATION OF DENGUE FEVER DISEASE USING A MACHINE LEARNING-BASED RANDOM FOREST ALGORITHM SETYAWAN, ARIF FITRA; Ariyanto, Amelia Devi Putri; Fikriah, Fari Katul
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 2 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8496

Abstract

Dengue Hemorrhagic Fever (DHF) is a tropical disease that often results in high morbidity and mortality rates. Early diagnosis of DHF is crucial to mitigate its adverse effects. However, manual diagnostic processes are often inefficient and prone to errors. This study aims to develop a DHF classification model using the Random Forest algorithm, which is expected to assist in the early diagnosis of this disease. The methodology used in this research is CRISP-DM (Cross-Industry Standard Process for Data Mining), which includes the stages of Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. Data was obtained from kaggle.com, and during the Data Preparation stage, missing values were removed, categorical features were encoded, data was normalized, and split into training and testing sets. The research results show that the Random Forest model has an accuracy of 88.5%, precision of 88.2%, recall of 65.2%, F1-score of 74.9%, and ROC AUC of 0.810. Feature importance analysis revealed that the Gender_Male and Body_Pain features have the largest contributions in DHF classification. Although the model demonstrated high accuracy and precision, the lower recall value indicates that some positive cases were missed, requiring further improvements. The Random Forest can be used as a tool for early DHF diagnosis, but further adjustments are necessary to enhance its performance. This research provides insights into the contributing factors for DHF diagnosis and the practical application potential of this model in medical decision support systems.
Impact of Statistical and Semantic Features Extraction for Emotion Detection on Indonesian Short Text Sentences Ariyanto, Amelia Devi Putri; Fikriah, Fari Katul; Setyawan, Arif Fitra
CommIT (Communication and Information Technology) Journal Vol. 19 No. 1 (2025): CommIT Journal (in press)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v19i1.11680

Abstract

The ability to detect emotions in short texts is crucial because interpreting emotions on platforms like Twitter can offer insight into social trends and responses to specific events. Additionally, examining emotions in product reviews assists companies in comprehending customer sentiment, allowing them to improve the quality of their products and services. Most research on Indonesian language emotion detection utilizes statistical feature extraction, with limited discussion on the impact of both statistical and semantic feature extraction. Thus, the research aims to detect emotions in short texts equipped with an analysis of the impact of statistical and semantic features. Analysis of the impact of statistical and semantic features on short texts is necessary to identify the most effective approaches, improve detection accuracy, and ensure that the developed systems can better handle the variety and complexity of informal language. The data used are a public dataset originating from Twitter texts and product review texts in e-commerce. The research utilizes statistical features such as Term Frequency Inverse Document Frequency (TF-IDF) and semantic features such as Bidirectional Encoder Representations from Transformers (BERT). The evaluation results show that using semantic features significantly improves the performance of emotion detection in short texts by 13–24%. It is higher than using statistical features. Deep Learning (DL) algorithms based on neural networks have also been proven to outperform Machine Learning (ML) algorithms in detecting emotions in short text. The experimental results and outlines show the potential directions for future development.
OPTIMIZING GPT AND INDOBERT FOR SENTIMENT ANALYSIS AND CONSUMER TREND PREDICTION ON LAZADA PRODUCT REVIEWS Setyawan, Arif Fitra; Nugraha, Rozaq Isnaini
JIKO (Jurnal Informatika dan Komputer) Vol 8, No 2 (2025)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i2.10066

Abstract

Sentiment analysis has become a vital approach in understanding customer opinions through textual reviews. One of the primary challenges in sentiment classification lies in class imbalance, where positive reviews often dominate the dataset. This imbalance causes machine learning models to be biased toward the majority class and underperform in detecting minority sentiments. To address this issue, this study applies the Synthetic Minority Oversampling Technique (SMOTE) and evaluates the performance of two Transformer-based models: Generative Pre-trained Transformer (GPT) as a baseline and IndoBERT as the primary model. The dataset consists of 12,704 product reviews from Lazada, obtained from the Kaggle platform, and is categorized into three sentiment classes (positive, neutral, negative). The data was split into 80% for training and 20% for testing. After preprocessing and applying SMOTE for data balancing, the fine-tuned IndoBERT model achieved the best performance with an accuracy of 88%, significantly outperforming GPT, which yielded only 47% accuracy in a zero-shot setting. These findings highlight the critical role of addressing data imbalance and selecting context-aware models for improving sentiment classification accuracy in Indonesian language texts
Implementasi inovasi energi hijau untuk mendukung kemandirian energi dalam penerangan jalan di Desa Surokonto Wetan Kecamatan Pageruyung Kabupaten Kendal Setyawan, Arif Fitra; Nugraha, Rozaq Isnaini
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 9, No 4 (2025): Juli
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v9i4.32309

Abstract

AbstrakPemanfaatan energi terbarukan menjadi langkah strategis dalam mendukung transisi energi yang berkelanjutan, terutama di wilayah pedesaan yang belum sepenuhnya terjangkau oleh infrastruktur listrik konvensional. Kegiatan pengabdian masyarakat ini bertujuan untuk mengimplementasikan inovasi teknologi Penerangan Jalan Umum Tenaga Surya (PJU-TS) sebagai solusi penerangan berbasis energi hijau di Desa Surokonto Wetan, Kecamatan Pageruyung, Kabupaten Kendal. Permasalahan utama yang dihadapi masyarakat setempat adalah minimnya pencahayaan jalan lingkungan, yang berdampak pada aspek keselamatan, aktivitas malam hari, dan rasa aman warga. Metode pelaksanaan meliputi identifikasi kebutuhan, perancangan sistem, instalasi PJU-TS, serta edukasi teknis kepada masyarakat. Sistem yang digunakan terdiri dari lampu LED berdaya 1500W dengan 288 chip, panel surya monocrystalline 50Wp, baterai lithium 12V/24Ah, dan sensor otomatis (LDR). Panel surya yang digunakan mampu menghasilkan energi yang terbarukan dan memerlukan proses perawatan berkala, seperti pembersihan debu serta pengecekan koneksi panel dan baterai untuk menjaga efisiensi konversi energi. Kegiatan dilaksanakan pada 17 April 2025 dengan melibatkan warga serta Ketua RT setempat. Hasil kegiatan menunjukkan bahwa sistem PJU-TS berfungsi dengan baik, memberikan pencahayaan selama ±11 jam per malam dengan radius penerangan mencapai ±15 meter. Respons masyarakat sangat positif terhadap penggunaan energi surya yang dinilai lebih efisien dan ramah lingkungan. Keberhasilan program ini menunjukkan bahwa penerapan teknologi tepat guna berbasis energi hijau dapat menjadi solusi alternatif untuk meningkatkan kualitas hidup masyarakat desa serta mendukung pencapaian Tujuan Pembangunan Berkelanjutan (SDGs), khususnya pada poin energi bersih dan terjangkau. Replikasi program serupa direkomendasikan untuk wilayah lain yang memiliki permasalahan sejenis. Kata kunci: desa; energi hijau; energi terbarukan; PJU-TS; pengabdian masyarakat; teknologi tepat guna. Abstract The utilization of renewable energy represents a strategic step in supporting the transition toward sustainable energy systems, particularly in rural areas that are not yet fully reached by conventional electricity infrastructure. This community service activity aims to implement an innovative Solar-Powered Street Lighting (PJU-TS) system as a green energy-based lighting solution in Surokonto Wetan Village, Pageruyung District, Kendal Regency. The main issue faced by the local community is the lack of environmental street lighting, which affects safety, nighttime activities, and residents' sense of security. The implementation methods included needs identification, system design, PJU-TS installation, and technical education for the community. The installed system consists of 1500W LED lights with 288 chips, a 50Wp monocrystalline solar panel, a 12V/24Ah lithium battery, and an automatic light sensor (LDR). The solar panel used generates renewable energy and requires periodic maintenance, such as dust cleaning and checking panel-battery connections to maintain energy conversion efficiency. The activity was conducted on April 17, 2025, involving local residents and the neighborhood chief. The results indicate that the PJU-TS system operated effectively, providing lighting for approximately 11 hours per night with a coverage radius of around 15 meters. Community responses were highly positive, as solar energy is perceived to be more efficient and environmentally friendly. This program also enhanced public awareness of renewable energy technologies. The success of this initiative demonstrates that the application of appropriate green energy technologies can be an alternative solution to improve the quality of rural life while contributing to the achievement of Sustainable Development Goals (SDGs), particularly Goal 7: Affordable and Clean Energy. Similar programs are recommended for replication in other regions facing comparable challenges. Keywords: appropriate technology; community service; green energy; PJU-TS; renewable energy; rural area.
CLASSIFICATION OF DENGUE FEVER DISEASE USING A MACHINE LEARNING-BASED RANDOM FOREST ALGORITHM SETYAWAN, ARIF FITRA; Ariyanto, Amelia Devi Putri; Fikriah, Fari Katul
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 2 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8496

Abstract

Dengue Hemorrhagic Fever (DHF) is a tropical disease that often results in high morbidity and mortality rates. Early diagnosis of DHF is crucial to mitigate its adverse effects. However, manual diagnostic processes are often inefficient and prone to errors. This study aims to develop a DHF classification model using the Random Forest algorithm, which is expected to assist in the early diagnosis of this disease. The methodology used in this research is CRISP-DM (Cross-Industry Standard Process for Data Mining), which includes the stages of Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. Data was obtained from kaggle.com, and during the Data Preparation stage, missing values were removed, categorical features were encoded, data was normalized, and split into training and testing sets. The research results show that the Random Forest model has an accuracy of 88.5%, precision of 88.2%, recall of 65.2%, F1-score of 74.9%, and ROC AUC of 0.810. Feature importance analysis revealed that the Gender_Male and Body_Pain features have the largest contributions in DHF classification. Although the model demonstrated high accuracy and precision, the lower recall value indicates that some positive cases were missed, requiring further improvements. The Random Forest can be used as a tool for early DHF diagnosis, but further adjustments are necessary to enhance its performance. This research provides insights into the contributing factors for DHF diagnosis and the practical application potential of this model in medical decision support systems.
Co-Authors Amelia Devi Putri Ariyanto Ariyanto , Amelia Devi Putri Fari Katul Fikriah Rozaq Isnaini Nugraha, Rozaq Isnaini