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All Journal Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Teknovasi : Jurnal Teknik dan Inovasi Mesin Otomotif, Komputer, Industri dan Elektronika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Informatika Kaputama (JIK) Jurnal Informasi dan Teknologi Vocatech : Vocational Education and Technology Journal JTIK (Jurnal Teknik Informatika Kaputama) JOURNAL OF INFORMATICS AND COMPUTER SCIENCE G-Tech : Jurnal Teknologi Terapan Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) Jurnal Pengabdian kepada Masyarakat Nusantara JINAV: Journal of Information and Visualization International Journal of Engineering, Science and Information Technology MALLOMO: Journal of Community Service Journal of Renewable Energy, Electrical, and Computer Engineering Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Teknologi Terapan and Sains 4.0 Jurnal Pengabdian Masyarakat Bangsa MEUSEURAYA : JURNAL PENGABDIAN MASYARAKAT Jurnal Informatika: Jurnal Pengembangan IT Jurnal Malikussaleh Mengabdi Journal of Advanced Computer Knowledge and Algorithms JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MICoMS)
Yesy Afrillia
Universitas Malikussaleh
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Classification Of Outpatient Visit Status Walking at Dr. Zubir Mahmud Hospital Using Algoritma C4.5 Fikria, Putri; Dinata, Rozzi Kesuma; afrillia, Yesy
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.865

Abstract

This study aims to classify the status of outpatient visits at RSUD Dr. Zubir Mahmud into three main categories, namely "Very Urgent", "Urgent", and "Not Urgent”, using the C4.5 algorithm. The web-based system uses the PHP programming language and MySQL database to ensure ease of implementation and efficient data management. The classification process is done by setting threshold parameters, calculating entropy, and the gain ratio to form an accurate and reliable decision tree. The results show that the C4.5 algorithm can classify patient visit data with a reasonably high accuracy rate, which is 93.75% for 2022 data and reaches 100% for 2023 data. In 2022 the “Very Urgent" category had 9 True Positives (TP); in 2023, the number remained consistent. However, in both years, there were also False Negatives in the same category, with 4 cases in 2022 and 5 cases in 2023. The "Urgent" and "Not Urgent" categories show suboptimal classification performance due to uneven data distribution, which causes the precision and recall values in these categories low. Model evaluation was conducted using evaluation metrics such as precision, recall, and F1 score. The evaluation results show that the model works very well in identifying high-priority categories, but further development is needed to improve classification in other categories. This system is expected to be a reliable tool in decision-making in health services, especially in determining the priority of patient services appropriately and efficiently. With further development, this system has the potential to be widely applied in various other hospitals.
Performance Analysis of API Protocol Models as Recommendations for Developers in Application Development Fhonna, Rizky Putra; Afrillia, Yesy; Ilhadi, Veri; Arif, Abdul Halim; Selian, Riko Ardiansyah
JINAV: Journal of Information and Visualization Vol. 5 No. 2 (2024)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav3041

Abstract

The evaluation of various API types reveals distinct strengths and weaknesses. REST APIs exhibit inefficient performance with high average response times and an error rate of approximately 14%, indicating potential delays and instability under load. SOAP APIs, with an average response time of 167 ms, perform better than REST in terms of speed but still lag behind GraphQL and have a slightly higher error rate of 14.80%. GraphQL demonstrates the fastest average response time at around 171 ms, offering high efficiency in data delivery, although its error rate is notably high at 15%, signaling a need for improved stability. RPC APIs, with an average response time of 238 ms, are less speedy compared to GraphQL and SOAP but excel in stability with a very low or zero error rate, making them highly reliable under high loads. Overall, GraphQL is optimal for applications requiring rapid data interaction, RPC is best suited for scenarios demanding high consistency and reliability, SOAP offers a middle ground, and REST may be appropriate for simpler, less demanding applications.
Job Vacancy Recommendation System using JACCARD Method On Graph Database Riza, Saiful; Fuadi, Wahyu; Afrillia, Yesy
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 3 (2025): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i3.2387

Abstract

In the rapidly evolving digital era, recommendation systems play a crucial role in helping users discover relevant information aligned with their preferences. PT Nirmala Satya Development, a company engaged in psychology and human resource development, faces challenges in utilizing big data consisting of 500 applicants, 500 job postings, and 500 job applications to generate accurate and relevant job recommendations. This study develops a job recommendation system using the Jaccard Coefficient method to measure similarity between users based on their job application history, implemented within a Neo4j graph database. The system models the relationships between entities through nodes and edges, allowing dynamic analysis using the Cypher Query Language. Testing on 237 users demonstrated that the majority received at least one relevant recommendation, with recall values often reaching 1.0, especially among users who had a single job target. The system achieved precision values ranging from 10% to 20%, which is considered acceptable given that ten recommendations are generated per user. The highest F1-score reached 0.33, although some users received F1 = 0 due to limited application history or unique preferences. Overall, the system effectively delivers personalized and efficient job recommendations, particularly for active users. This research also proves that combining the Jaccard Coefficient with a graph database structure is a powerful approach to representing and analyzing complex relationships between users and job postings in a modern recruitment platform.
Detection of Qur’anic Ikhfa Patterns in Digital Images Using Binary Similarity Distance Measures (BSDM) with 3W-Jaccard Formula Julianansa, Ririn; Fadlisyah; Yesy Afrillia
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9814

Abstract

Recitation rules in the Qur'anic script form various visual patterns. One of the selected rules for this study is the Ikhfa pattern. Ikhfa is a recitation rule pronounced subtly when the nun sukun (نْ) or tanwin (ـَــًـ, ـِــٍـ, ـُــٌـ) is followed by one of 15 specific letters, namely: ta’ (ت), tsa’ (ث), jim (ج), dal (د), dzal (ذ), za’ (ز), sin (س), syin (ش), shad (ص), dhad (ض), tha’ (ط), zha’ (ظ), fa’ (ف), qaf (ق), and kaf (ك). In this study, the primary challenge is the difficulty of automatically detecting the Ikhfa pattern in both digital and printed Qur'anic texts. This challenge arises from the subtlety of the recitation rule, which makes it difficult to distinguish from other recitation patterns. To address this, the Ikhfa pattern is detected using image processing techniques, and pattern classification is performed using the Binary Similarity and Distance Measures (BSDM) method. The results indicate that the pattern detection system, employing BSDM with the 3W-Jaccard formula, achieved a detection rate of 83.84%. This suggests that the 3W-Jaccard formula is an effective approach for detecting similar recitation patterns. One advantage of the 3W-Jaccard formula is its ability to recognize patterns with a relatively small amount of reference data, making it highly suitable for implementation in the detection system.
Stunting Risk Detection and Food Recommendation via Maternal Diagnosis Using the CF Method Kautsar, Al; Asrianda, Asrianda; Afrillia, Yesy
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9949

Abstract

Stunting in children often stems from maternal health conditions during pregnancy. This study aims to develop an intelligent rule-based IF–THEN system using the Certainty Factor method as a decision-support tool for the early detection of stunting risk factors. The detection is performed indirectly by diagnosing maternal health conditions during pregnancy. The knowledge base was constructed through interviews with obstetricians and nutritionists, encompassing 20 symptoms categorized into three primary conditions namely Chronic Energy Deficiency (CED), anemia, and preeclampsia. A total of 119 pregnant women from 11 villages in Muara Satu District participated as respondents. Implementation results revealed that among the respondents, 20 were identified with CED, 96 had anemia, and 3 exhibited signs of preeclampsia. Based on Certainty Factor (CF) calculations, the confidence distribution for CED included 2 respondents with CF <50%, 5 respondents within the 50–80% range, and 13 respondents with CF >80%. For anemia, 1 respondent had a CF value <50%, 4 fell within the 50–80% range, and 91 respondents had CF values above 80%. Meanwhile, for preeclampsia, all respondents exceeded the 50% CF threshold, with 1 respondent in the 50–80% range and 2 respondents >80%. In addition to diagnosis, the system provides tailored meal recommendations (breakfast, lunch, and dinner) based on the identified health conditions. Expert validation indicated a 90% agreement rate. However, results still require confirmation through clinical examinations and consultations to ensure medical accuracy.
Location Entity Recognition in Instagram Captions Using Support Vector Machine Algorithm Arifa, Cut Hilma; Adek, Rizal Tjut; Afrillia, Yesy
VOCATECH: Vocational Education and Technology Journal Vol 7, No 1 (2025): August
Publisher : Akademi Komunitas Negeri Aceh Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38038/vocatech.v7i1.238

Abstract

AbstractThe rapid advancement of digital technology has significantly influenced productivity and facilitated access to information in daily life, particularly through the widespread use of social media. Instagram is one of the most popular platforms, where text in captions often contains location-related information that can be utilized for spatial analysis. This study aims to identify and classify location entities in Instagram captions using Support Vector Machine algorithm combine with rule-based Named Entity Recognition approach. The method involved linguistic feature extraction based on explicit spatial context, data labeling, model training, and performance evaluation using standard classification metrics: accuracy, precision, recall, and f1-score. Dataset consists of 400 captions primarily written in Indonesian, though some contain mixed-language elements such as foreign term or regional dialect. The dataset is divided into 70% training data ad 30% testing data. Experimental results show that model achieved an accuracy of 90,83%, precision of 97,01%, recall of 87,84%, and f1-score of 92,90%. Evaluation of three NER rules (exact match keyword, prepositional patterns, and descriptive structures) indicates that the combination of all rules yields the highest f1-score (89%), while the best-performing individual rule is the prepositioning pattern (74%). These results demonstrated strong performance in processing varied and unstructured Instagram captions. The combinations of SVM and NER rule-based prove effective in identifying and classifying spatial information into two classes Contains Location and No Location. This approach shows potential for implementation in text-based spatial analysis systems, such as location-based recommendation systems, geographic mapping, and location-based decision support systems. AbstrakPerkembangan teknologi digital yang pesat secara signifikan berpengaruh meningkatkan produktivitas dan kemudahan akses informasi dalam kehidupan sehari-hari, salah satunya penggunaan media sosial yang semakin meluas. Instagram merupakan salah satu platform yang banyak digunakan, dimana teks dalam caption memiliki informasi terkait lokasi yang dapat dimanfaatkan untuk analisis spasial. Penelitian ini bertujuan untuk mengidentifikasi dan mengklasifikasikan entitas lokasi dalam caption Instagram menggunakan algoritma Support Vector Machine (SVM) dengan pendekatan Named Entity Recognition (NER) rule-based. Metode yang digunakan meliputi ekstraksi fitur berbasis linguistik dengan konteks spasial eksplisit, lebelisasi data, pelatihan model, serta evaluasi kinerja model menggunakan matriks klasifikasi: akurasi, presisi, recall dan f1-score. Dataset terdiri dari 400 caption umumnya berbahasa Indonesia, namun terdapat unsur bahasa campuran seperti istilah asing atau bahasa daerah. Fokus utama penelitian diarahkan pada pengolahan dan pemahaman teks berbahasa Indonesia. Dataset dibagi menjadi 70% data training dan 30% data testing. Hasil pengujian menunjukkan bahwa model mendapatkan akurasi sebesar 90,83%, presisi 97,01%, recall 87,84% dan f1-score 92,90%. Evaluasi terhadap tiga rule NER (exact match keyword, pola preposisi, dan struktur deskriptif) menunjukkan bahwa pengenalan entitas berdasarkan gabungan seluruh rule memberikan f1-score tertinggi (89%), sementara rule individual terbaik adalah pola preposisi (74%). Nilai ini menunjukkan kinerja yang cukup baik dalam pengolahan caption Instagram yang variatif dan tidak terstruktur. Kombinasi metode SVM dan NER rule-based terbukti efektif dalam mengidentifikasi dan mengklasifikasi informasi spasial dalam dua kelas Contain Location dan No Location. Pendekatan ini berpotensi diterapkan pada sistem analisis spasial berbasis teks, seperti sistem rekomendasi lokasi, pemetaan geografis, dan pendukung keputusan berbasis lokasi.
Clustering of Aquaculture Productivity Villages in East Aceh Using the K-Means Algorithm Arif, M. Arif Saputra; Dinata, Rozzi Kesuma; Afrillia, Yesy
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10102

Abstract

This study aims to classify villages based on the level of pond utilization and to develop a web-based application for categorizing aquaculture areas in East Aceh Regency. In contrast to traditional definitions based on harvest volume, this research defines productivity functionally—whether the pond area is actively managed or abandoned. The dataset consists of 146 villages and includes five primary variables: number of fish farmers, total pond area, number of pond plots, productive pond area, and abandoned pond area. Clustering was conducted using the K-Means algorithm, resulting in two main groups: productive and non-productive villages. Validation through the Silhouette Score revealed that using k = 2 yielded the highest score of 0.7576, indicating the most optimal clustering structure. The analysis showed that 92% of villages were categorized as productive, while 8% fell into the non-productive cluster. These two clusters differ significantly in terms of land utilization ratios and the number of active aquaculture workers. The findings not only offer a more refined spatial insight but also serve as a basis for the Department of Marine Affairs and Fisheries in formulating aquaculture zoning, revitalization programs, and more targeted resource allocation.
IoT-Based Adaptive Room Temperature Monitoring and Energy Optimization System Using NodeMCU ESP8266 Aswandi, Sakti; Rizal, Rizal; Afrillia, Yesy
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10052

Abstract

This study presents the development of an IoT-based room temperature monitoring and AC control system at the Faculty of Engineering, Universitas Malikussaleh, using NodeMCU ESP8266, DHT11 sensor, PIR sensor, and IR LED for real-time automation via a Firebase web interface. The system automatically adjusts AC operation based on room temperature and occupancy, with daily logic resets to accommodate dynamic conditions. Testing conducted over one week demonstrated effective temperature stabilization within 25–26°C with ±2°C fluctuations and significant energy savings by deactivating the AC when the temperature drops below 25°C or the room is unoccupied. The PIR sensor supports a detection range of up to 7 meters, allowing scalability for different room sizes. User evaluation involving five respondents reported satisfaction scores of 4.2 for comfort and energy efficiency, though aspects such as the web interface (3.6) and system information display (2.6) require improvement. Overall, the system effectively enhances energy efficiency, ensures room comfort, and provides flexible control for users, supporting the smart classroom concept. Future development is directed toward the use of more accurate sensors like DHT22 or DS18B20, improved network stability, and integration with virtual assistants for voice-controlled operation.
Optimalisasi Pelayanan Kesehatan Sebagai Inovasi Pelayanan Publik Melalui Pendampingan Pemanfaatan Website Di Puskesmas Dewantara Afrillia, Yesy; Faridhatul Ulva, Ananda; Fidyati, Fidyati; Wardana, Ade Bagus; Zuhra, Elviza
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 4 No. 4 (2023): Jurnal Pengabdian kepada Masyarakat Nusantara (JPkMN)
Publisher : Lembaga Dongan Dosen

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

Abstract

Puskesmas Dewantara terletak pada wilayah Kampus Utama Universitas Malikussaleh. Yang kita ketahui saat ini mahasiswa di kampus Universitas Malikussaleh sebagai cerminan Indonesia mini yang diisi oleh mahaiswa dari sabang hingga marauke. Hal ini menjadi landasan bahwa perlu adanya optimalisasi pelayanan kesehatan sebagai inovasi pelayanan publik dengan pemanfaatan website sebagai media penyampaian suatu informasi yang terpercaya kepada masyarakat secara umum. Tujuan dari kegiatan ini ialah mendorong pemanfaatan inovasi teknologi informasi dalam program pembangunan desa untuk mendukung terwujudnya smart village pada lingkup pelayanan publik dari optimalisasi pelayanan kesehatan. Metode yang digunakan dalam penyelesaian masalah ini ialah dengan observasi, koordinasi serta teknik pengumpulan data. Analisis data menggunakan teknik analisis data interaktif. Hasil inovasi yang diberikan oleh tim pengabdian kepada masyarakat Universitas Malikussaleh yaitu berupa Website kepada mitra yaitu UPTD Puskesmas Dewantara sangat diterima karena ini menjadi suatu kebutuhan yang tepat bagi inovasi yang belum mereka miliki baik bagi peningkatan mutu akreditasi instansi.
Klasifikasi Kesehatan Mental Remaja Tingkat SMA di Kota Lhokseumawe Menggunakan Algoritma Random Forest Afrillia, Yesy; Fhonna, Rizky Putra; Rahma, Mutiara
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 6 No. 3 (2025): Edisi Juli - September
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jpkmn.v6i3.6377

Abstract

Kesehatan mental menjadi isu penting dalam perkembangan seorang remaja yang berdampak pada prestasi akademik, hubungan sosial, kualitas hidup, dan kesejahteraan secara keseluruhan. Dengan data dari 229 remaja di Kota Lhokseumawe, penelitian ini mengembangkan model klasifikasi kesehatan mental remaja berbasis Random Forest. Data dikategorikan ke dalam empat kelas gangguan mental (Tidak Ada Indikasi, Level 1-3) berdasarkan indikator HSCL-25 dan motivasi belajar. Metode penelitian mencakup studi literatur, pengumpulan data, preprocessing data, training model, serta evaluasi kinerja menggunakan metrik akurasi evaluasi. Hasil menunjukkan bahwa model mencapai akurasi sebesar 86,96%, dengan F1-score masing-masing sebesar 0,94 Tidak Ada Indikasi), 0,81 (level 1), 0,75 (level 2), dan 0,00 (level 3). Analisis feature importance mengidentifikasi bahwa kesepian, perasaan tidak berharga, dan kehilangan harapan merupakan fitur utama paling berpengaruh dalam klasifikasi. Meskipun model mampu mengklasifikasikan sebagian besar data dengan baik, masih terdapat kesalahan klasifikasi pada beberapa tingkat gangguan mental. Model, yang dibangun menggunakan API Flask dan Laravel dalam sistem berbasis web, memungkinkan remaja mengisi kuesioner dan memperoleh hasil klasifikasi secara otomatis. Dengan demikian, model Random Forest dapat digunakan sebagai alat bantu klasifikasi yang efektif dalam mendeteksi potensi gangguan mental remaja. Hasil penelitian ini dapat menjadi dasar bagi institusi pendidikan dalam merancang strategi intervensi yang lebih tepat sasaran
Co-Authors Abadi, Sabani Abdul Hadi Abil Khairi Adek, Rizal Tjut Aldo januansyah. H Ananda Faridhatul Ulva Annas, Muhammad Aqmal, Jamalul Arif, Abdul Halim Arif, M. Arif Saputra Arifa, Cut Hilma Asmi, Nurul Annisa Asrianda Asrianda Asrifan, Andi Asrillah Asrillah Aswandi, Sakti Ayu Indah Lestari Berutu, Indah Fachlira Bustami Bustami Cut Agusniar Dahlan Abdullah Dasril Dasril David Sarana Deassy Siska EDI YUSUF, EDI Effan Fahrizal Ekamaida, Ekamaida Elvina Mutiara Vina Eri Saputra eva darnila, eva darnila Fadlisyah Fadlisyah Fadlisyah Faiz Fadhilla Fakhruddin Ahmad Nasution Farhan Dika Fatika, Dian Fidyati, Fidyati Fikria, Putri Fuadi, Wahyu Gilang Ramadhan Purba Hafidh Rafif, Teuku Muhammad Harahap, Ilham Taruna Herman Fithra Hidayat, Amam Taufiq ilham - sahputra Ilsa Hidayat Intan Putri Dinanti Jamalul Aqmal Julianansa, Ririn Kasihan Muhammad Fajar Kautsar, Al Khairuni Khairuni Lidya Rosnita Mahadika Luqman Mahesa Reglisalo Muhammad Fikry Muhammad Ikhwanus Muhammad Iqbal Muhammad Muhammad Muhammad Yusuf Mukhlis Mukhlis Mukti Qamal Muzaffar Rigayatsyah Muzaffar Rigayatsyah NELI SUSANTI, NELI Nurdin Nurdin Nurqamarina Rahma, Mutiara Rahmawati, Rahmawati Rini Meiyanti Risawandi, Risawandi Riza, Saiful Rizal Rizal Rizal Rizal S.Si., M.IT, Rizal Rizky Putra Fhonna Rozzi Kesuma Dinata Safriand, Safriand Sari, Rika Yulia Sayed Fachrurrazi Selian, Riko Ardiansyah Siregar, Winda Ramadhani Sofyan Sofyan Suci Ramadani Sujacka Retno Teuku M. Arief Afwan Tursina Dewi Uliana, Lisa Ulva Ilyatin Veri Ilhadi Wahyu Fuadi Wahyu Fuadi Wardana, Ade Bagus Widari, Liz Ayu Winda Yanti Yusril Zahratul Fitri, Zahratul Zara Yunizar Zuhra, Elviza Zulfan