Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Techno.Com: Jurnal Teknologi Informasi Poltekita: Jurnal Pengabdian Masyarakat Scientific Journal of Informatics
Muhamad Anbiya Nur Islam
Unit Pelaksana Teknis Teknologi Informasi Dan Komunikasi, Universitas Negeri Semarang, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Health Professions Nursing Public Health

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Analisis dan Perancangan Geographic Information System pada Pelacakan Lokasi Kurir Secara Real Time Muhamad Anbiya Nur Islam; Much Aziz Muslim; Hendi Susanto
Techno.Com Vol 17, No 2 (2018): Mei 2018
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (707.02 KB) | DOI: 10.33633/tc.v17i2.1686

Abstract

Kecepatan akses informasi menjadi pembahasan cukup penting dalam pembuatan aplikasi. Teknologi yang mulai merambah pada dunia serba mobile menjadikan kecepatan dan kepraktisan akses informasi menjadi prioritas utama dalam pelayanan fitur sebuah aplikasi. Aktivitas logistik yang melibatkan banyak komponen pengguna memerlukan suatu sistem informasi yang dapat memberikan layanan informasi cepat dan akurat. Pada penelitian ini penulis membuat Real time system berbasis Geographic Information System, untuk mengakomodasi kebutuhan komunikasi data lokasi antara kurir dan outlet. Metode yang digunakan dalam penelitian ini memanfaatkan Aplication Programming Interface yang disediakan oleh Google, yaitu Google Maps API. Data spasial yang digunakan pada penelitian ini mengambil dari data Maps milik Google. Hasil penelitian ini adalah suatu sistem yang saling berkomunikasi mengirim data spasial berupa latitude, longitude, distance, time estimation, route, dan real time marker. Penggunaan GIS berbasis Google Maps API ini sangat efektif dalam membantu estimasi dan monitoring proses pengiriman dan jual beli.
Be Hero For Zero Tuberculosis: Peran Remaja Melalui SIKRIBO dalam Mewujudkan "End TB": Be Hero for Zero Tuberculosis: The Role of Youth Through Sikribo in Realizing “End TB” Sri Ratna Rahayu; Widya Hary Cahyati; Intan Zainafree; Eko Farida; Aufiena Nur Ayu Merzistya; Tatik Atmini; Anggun Dessita Wandastuti; Isbandi Isbandi; Andreas Wilson Setiawan; Atika Aulia; Nur Wahidah; Muhamad Anbiya Nur Islam; Alvy Fajri; Minhajul Mubarok; Fitria Ningsih; Mona Subagja; Muhamad Zakki Saefurrohim
Poltekita: Jurnal Pengabdian Masyarakat Vol. 3 No. 2 (2022): April-Juni
Publisher : Pusat Penelitian & Pengabdian Masyarakat Poltekkes Kemenkes Palu

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1022.061 KB) | DOI: 10.33860/pjpm.v3i2.849

Abstract

The TB case notification rate (CNR) in Semarang City has decreased from 2019 of 258 per 100,000 population to 155 per 100,000 population in 2020. The main focus in controlling tuberculosis cases is case detection in the community. The purpose was to increase TB knowledge and provide training on TB screening methods to adolescent cadres using the SIKRIBO application. The methods used in this community service are health education and Training of Trainers (ToT). The participants were 27 adolescent representatives from the Adolescent Integrated Healthcare Center working areas of the Sekaran Public Health Center, Semarang City. The results showed that adolescent cadres' average score knowledge about tuberculosis disease and screening improves after receiving TB health education and adolescent cadres can operate the application properly. The SIKRIBO application can be a means to facilitate the discovery of TB suspects in the community and is expected to be applied in various health facilities.   ABSTRAK Angka notifikasi kasus (CNR) TB di Kota Semarang mengalami penurunan dari tahun 2019 sebesar 258 per 100.000 penduduk menjadi 155 per 100.000 penduduk pada tahun 2020. Fokus utama dalam mengendalikan kasus TB merupakan penemuan kasus di masyarakat. Rendahnya penemuan kasus TB berdampak pada tingginya angka penularan TB. Tujuan pengabdian masyarakat ini untuk meningkatkan pengetahuan TB dan memberi pelatihan terkait cara skrining TB melalui aplikasi SIKRIBO pada kader remaja. Metode yang digunakan pada pengabdian masyarakat ini adalah penyuluhan kesehatan dan Training of Trainer (ToT). Jumlah partisipan sebanyak 27 remaja perwakilan dari Posyandu Remaja dan Posyandu Pesantren wilayah Puskesmas Sekaran, Kota Semarang. Hasil kegiatan bahwa terdapat peningkatan rata-rata skor pengetahuan terkait penyakit TB dan skrining TB pada kader remaja setelah diberi penyuluhan kesehatan TB dan kader remaja dapat mengoperasikan aplikasi SIKRIBO dengan baik. Aplikasi SIKRIBO dapat menjadi sarana untuk mempermudah penemuan suspek TB di masyarakat dan diharapkan dapat diterapkan di berbagai fasilitas kesehatan.
Improving Sentiment Analysis with a Context-Aware RoBERTa–BiLSTM and Word2Vec Branch Wahyu Hardyanto; Nila Prasetya Aryani; Defin Andestian; Sugiyanto; Wahyu Setyaningrum; M Fadil Mardiansyah; Muhamad Anbiya Nur Islam; Aji Purwinarko
Scientific Journal of Informatics Vol. 12 No. 4: November 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i4.35918

Abstract

Purpose: We improve the accuracy of Twitter/X sentiment analysis with a hybrid model combining Word2Vec and the Robustly Optimized BERT Pretraining Approach (RoBERTa). However, Twitter/X text is noisy (slang/OOV) and ambiguous, so the performance of the pre-trained transformer decreases. Word2Vec is also limited to local contexts. Integrative studies of both are still limited. The idea is that Word2Vec is strong for slang/novel vocabulary (distributional semantics), while RoBERTa excels in contextual meaning; combining the two mitigates each other's weaknesses. Methods: The Sentiment140 dataset contains 1.6 million balanced tweets. The split is stratified; Word2Vec is trained solely on the training data. RoBERTa is pretrained (frozen in the first stage, then fine-tuned with some layers in the second stage). The Word2Vec and RoBERTa vectors are concatenated and processed using Bidirectional Long Short-Term Memory (BiLSTM) with sigmoid activation. Training utilizes TensorFlow and the Adam optimizer, incorporating dropout and early stopping. The decision threshold is optimized during the validation process. Result: The hybrid model achieved an accuracy of 88.09%, an F1-score of 88.09%, and an Area Under the Curve (AUC) ≈ 95.19% on the Receiver Operating Characteristic (ROC). No overfitting was observed, and the hybrid model outperformed both single baselines. The confusion matrix and ROC curve corroborate the findings. Novelty: The novelty lies in the fusion of distributional and contextual representations with a structured fusion mechanism. Limitations: Computational requirements and hyperparameter tuning are not yet extensive. Further directions: Systematic hyperparameter search and cross-validation across other large sentiment datasets to assess generalization.
Co-Authors Aji Purwinarko, Aji Alvy Fajri Andreas Wilson Setiawan Anggun Dessita Wandastuti Atika Aulia Aufiena Nur Ayu Merzistya Defin Andestian Farida, Eko Fitria Ningsih Hendi Susanto Intan Zainafree, Intan Isbandi Isbandi M Fadil Mardiansyah Minhajul Mubarok Mona Subagja Much Aziz Muslim Nila Prasetya Aryani Nur Wahidah Rr. Sri Ratna Rahayu Saefurrohim, Muhamad Zakki Sugiyanto Tatik Atmini Wahyu Hardyanto Wahyu Setyaningrum Widya Hary Cahyati