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All Journal BIOTROPIA - The Southeast Asian Journal of Tropical Biology Jurnal Tekno Kompak Journal of Informatics, Information System, Software Engineering and Applications (INISTA) JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Premium: Insurance Business Journal Journal of Computer Science and Informatics Engineering Jurnal Pengabdian Masyarakat Ilmu Komputer
Istiqomah, Nalar
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Environmental Science Immunology & microbiology Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Veterinary

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Sistem Pakar Rekomendasi Produk Asuransi Jiwa Berdasarkan Profil Nasabah menggunakan Algoritma Forward Chaining Istiqomah, Nalar; Novika, Fanny
Premium Insurance Business Journal Vol. 11 No. 1 (2024): PREMIUM INSURANCE BUSINESS JOURNAL
Publisher : P3M Trisakti School of Insurance (TSI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35904/premium.v11i1.59

Abstract

The aim of this research is to build a web-based expert system that can provide life insurance product recommendations. This expert system can be used to help people choose life insurance products that suit their own profile. This research began by conducting a literature study to determine the type of life insurance product and compiling a questionnaire which would later be given to experts. The results of this questionnaire are used as the basis for expert system knowledge which will later be processed using a forward chaining algorithm. From this process, 6 rules were obtained that can be used to recommend life insurance products. After the rules are obtained, a web based expert system is built using the PHP programming language. The expert system was tested using the black box method. From the test results, it is known that the system can carry out its functions well. Therefore, the website can be published on the internet and can be accessed at consurence.id.
Pengenalan Coding Membuat Game pada Siswa Sekolah Dasar menggunakan Scratch Istiqomah, Nalar; Fanny Novika
JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Vol. 5 No. 3 (2024)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/jurpikat.v5i3.1827

Abstract

In the digital age, both adults and children are accustomed to using gadgets with Internet capabilities. The things that children access the most are games. In fact, using gadgets too often will have a negative impact on both physical and mental health. Instead of just playing games, children should be encouraged to learn how to make games. This has many benefits, including practicing computational thinking, problem solving, and perseverance. Therefore, this PkM activity was conducted with the aim of introducing coding skills to children. This activity was conducted online using Zoom. Participants learned together to create games using Scratch, such as those available at bit.ly/GameKasir. Of the 19 participants who attended, 100% were satisfied with the activities provided and were even interested in learning more about coding. This shows that PkM activities can introduce coding to children in a fun way, and can even motivate participants to continue learning coding.
VIRTUAL TRAINING PELATIHAN CODING UNTUK TENAGA PENDIDIK SD, SMP DAN SMA Istiqomah, Nalar; Novika, Fanny
Jurnal Pengabdian Masyarakat Ilmu Komputer Vol. 2 No. 1 (2025): Januari
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70248/jpmik.v2i1.1556

Abstract

Berdasarkan survey yang telah dilakukan Microsoft YouthSpark #WeSpeakCode dari 1850 siswa, coding lebih banyak ingin diketahui siswa sebanyak 91%. Sayangnya, hanya separuhnya siswa yang memiliki kesempatan belajar coding di sekolah, baik sebagai intrakulikuler maupun ekstrakulikuler. Selain itu, tidak banyak guru dan orang tua yang mempunyai kemampuan coding. Pelatihan coding menggunakan Scratch telah berhasil dilaksanakan dengan tujuan memperkenalkan keterampilan dasar coding kepada guru dan orang tua. Aplikasi Scratch dipilih karena mudah digunakan oleh siapa saja tanpa memerlukan pemahaman mendalam tentang bahasa pemrograman. Melalui kegiatan ini, peserta mampu memahami konsep computational thinking yang menjadi dasar dalam pembelajaran coding. Selain itu, mereka juga berhasil membuat flowchart untuk merancang game sederhana menggunakan Scratch. Berdasarkan hasil kuisioner, seluruh peserta menyatakan puas dengan pelatihan ini dan berharap adanya program lanjutan untuk mempelajari coding lebih mendalam. Hasil ini menunjukkan bahwa pelatihan telah berhasil memperkenalkan keterampilan coding secara efektif sekaligus memotivasi guru dan orang tua untuk mengajarkan coding kepada anak-anak atau siswa mereka.
Comparative Performance of IndoBERT and IndoLEM Baseline Models for Post-Disaster Health Information Extraction from Indonesian Online News Istiqomah, Nalar; Novika, Fanny
Journal of Computer Science and Informatics Engineering Vol 4 No 3 (2025): July
Publisher : Ali Institute of Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/cosie.v4i3.1174

Abstract

Natural disasters often have significant impacts on public health, yet systematic monitoring of post-disaster diseases in Indonesia remains limited. This study compares the performance of two Named Entity Recognition (NER) models in extracting health impacts, affected locations, and disaster types from Indonesian-language online news articles. The first model is IndoBERT, fine-tuned using 1,137 manually validated disaster-related news articles. The second comprises baseline models from the IndoLEM benchmark, namely mBERT and XLM-RoBERTa, without domain-specific training. Evaluation results show that IndoBERT outperforms the baseline models, achieving 90.00% accuracy and an F1-score of 88.26%, compared to mBERT (72.93%) and XLM-R (76.44%). Further analysis of the extracted entities reveals spatial and temporal disease trends: floods in Java are consistently associated with diarrhea and skin diseases, while volcanic eruptions in eastern Indonesia are linked to respiratory infections and hypertension. These findings highlight the importance of selecting appropriate models to support data-driven public health monitoring systems in disaster-prone regions
Sentiment Analysis Penyedia layanan Asuransi dari Media Sosial Twitter Istiqomah, Nalar; Novika, Fanny
Jurnal Tekno Kompak Vol 18, No 1 (2024): FEBRUARI
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jtk.v18i1.3465

Abstract

Abstrak− Tujuan dari penelitian ini adalah menerapkan analisis sentimen untuk mengevaluasi pandangan masyarakat terhadap penyedia layanan asuransi kesehatan, sehingga dapat memahami reputasi penyedia layanan asuransi. Penelitian ini menggunakan algoritme Naïve Bayes untuk mengidentifikasi sentimen pengguna Twitter terkait penyedia layanan asuransi kesehatan dan mengevaluasi akurasi hasilnya. Penelitian ini dilakukan untuk mendapatkan informasi sentimen masyarakat tentang perspektif penyedia layanan asuransi kesehatan melalui media sosial twitter. Pertumbuhan pengguna Twitter yang mencapai lebih dari 500 juta twit setiap hari memberikan potensi Big Data untuk mengevaluasi pandangan masyarakat terhadap asuransi kesehatan. Penelitian ini menggunakan metode penelitian deskriptif kualitatif dengan dukungan bahasa pemograman Python. Populasi penelitian ini mencakup semua twit yang diposting oleh pengguna di Indonesia. Kami menggunakan metode purposive sampling, yaitu pemilihan sampel berdasarkan kriteria tertentu yang sesuai dengan tujuan penelitian, seperti twit yang berhubungan dengan penyedia layanan asuransi kesehatan. Data yang digunakan adalah data primer, yaitu twit dari pengguna Twitter di Indonesia yang berkaitan dengan penyedia layanan asuransi kesehatan. Pengumpulan data dilakukan melalui web scraping dari aplikasi Tweet Harvest, dilanjutkan dengan proses labeling, dan kemudian data dipraproses melalui tahapan pembersihan, tokenisasi, penyaringan, dan stemming. Terakhir, algoritme Naïve Bayes digunakan untuk analisis sentimen. Dari proses pengambilan data, kami berhasil mengumpulkan 31.190 data, yang kemudian disaring menjadi 1.483 data yang hanya berupa hasil review. Pada tahap pelabelan, 889 twit mendapatkan label positif, sementara 594 twit mendapatkan label negatif. Didapatkan akurasi dari algoritme Naïve Bayes adalah 74.41%. Hasil ringkasan twit menggambarkan pandangan yang beragam terkait BPJS Kesehatan dan asuransi kesehatan swasta. Terdapat pandangan positif terhadap BPJS Kesehatan, termasuk premi yang terjangkau, cakupan penyakit kritis, dan pendaftaran tanpa medical check-up. Namun, ada kritik terhadap prosedur pengobatan yang dianggap rumit, kesulitan dalam menghentikan keanggotaan, dan perdebatan tentang prinsip gotong royong. Di sisi lain, asuransi kesehatan swasta mendapatkan pandangan positif karena prosedur yang lebih sederhana, antrian cepat, dan pilihan produk yang sesuai dengan penghasilan individu. Namun, terdapat juga pandangan negatif, termasuk gangguan telemarketing, kasus gagal bayar oleh penyedia asuransi, dan konsumen yang merasa tertipu ketika asuransi pendidikan beralih menjadi asuransi kesehatan tanpa persetujuan mereka.Kata Kunci: BPJS Kesehatan; Naïve Bayes; Penyedia layanan Asuransi; Sentiment Analysis; Twitter
Extracting Post‑Disaster Health Impact Information from News Reports Using Named Entity Recognition Istiqomah, Nalar; Novika, Fanny
Journal of INISTA Vol 7 No 2 (2025): May 2025
Publisher : LPPM Institut Teknologi Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/inista.v7i2.1814

Abstract

Natural disasters have a significant impact on public health, giving rise to various post-disaster illnesses. This study presents an automated information‑extraction framework based on Named Entity Recognition (NER), leveraging the IndoBERT model to identify disaster types, health impacts, and affected locations from online news reports. Data were gathered via web scraping from multiple reputable news portals and subsequently processed through tokenization, stop‑word removal, and lemmatization. Extracted entities were visualized via bar charts and word clouds to reveal disease patterns associated with each disaster type. Results indicate that floods have a significant public health impact, with skin diseases being the most prevalent, followed by diarrhea, fever, influenza, and Acute Respiratory Infections (ARIs). Volcanic eruptions are linked to health conditions such as ARI, hypertension, diarrhea, and influenza, whereas earthquakes show strong correlations with diarrhea, ARI, skin diseases, and fever. Droughts and landslides are closely associated with diarrheal outbreaks due to compromised sanitation resulting from limited access to clean water. Although less frequently reported, tsunamis also exhibit a notable association with cases of diarrhea. The proposed method achieves 90 % accuracy and an 88 % F1‑score. These findings confirm the effectiveness of our NER-based approach in detecting causal relationships between disasters and health outcomes, providing valuable insights for policymakers and healthcare professionals in designing targeted post-disaster mitigation and response strategies.
Fire Spot Identification Based on Hotspot Sequential Pattern and Burned Area Classification Sitanggang, Imas Sukaesih; Istiqomah, Nalar; Syaufina, Lailan
BIOTROPIA Vol. 25 No. 3 (2018): BIOTROPIA Vol. 25 No. 3 December 2018
Publisher : SEAMEO BIOTROP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11598/btb.2018.25.3.676

Abstract

Indonesia has the world's largest tropical peatlands of about 14.9 million hectares that have important life support roles. However, fire frequently occurs in peatlands. According to experts and field forest firefighters, fire hotspots that appear in a sequence of two to three days at the same location have a high potential of becoming a forest fire. This study aimed to determine the sequential patterns of hotspot occurrences, classify satellite image data and identify the fire spots. Fire spot identification was done using hotspot sequence patterns that were overlaid with burned area classification results. Sequential pattern mining using the Prefix Span algorithm was applied to identify sequences of hotspot occurrence. Maximum Likelihood method was applied to classify Landsat 7 satellite images toward identifying burned areas in Pulang Pisau and Palangkaraya in Central Kalimantan and Pontianak in West Kalimantan. Sequence patterns were overlaid with image classification results. The study results show that in Pulang Pisau, 26.19% of sequence patterns are located in burned areas and 72.62% sequence patterns were found in the buffer of burned area within a radius of one kilometer. As for Palangkaraya, there were 62.50% sequence patterns located in burned areas and 87.50% sequence patterns in the buffer of burned area within the radius of one kilometer. In total, there were 72.62% and 87.50% fire hotspots recorded in Pisau and Palangkaraya, respectively, which are strong indicators of peatland fires.
Co-Authors Fanny Novika Imas Sukaesih Sitanggang Lailan Syaufina Novika, Fanny