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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Informatika dan Teknik Elektro Terapan Sistemasi: Jurnal Sistem Informasi INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JURTEKSI Jurnal Pengabdian Kepada Masyarakat MEMBANGUN NEGERI Journal of Computer Science and Engineering (JCSE) Jurnal Teknik Informatika (JUTIF) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Lentera Pengabdian
Isnaini, Khairunnisak Nur
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Social Sciences Other

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Security Analysis of Simpel Desa using Mobile Security Framework and ISO 27002:2013 Isnaini, Khairunnisak Nur; Suhartono, Didit
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 7 No 1 (2023): February 2023
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v7i1.18742

Abstract

The Personal Identification Number or KTP is prone to be stolen and used by unwanted parties, this is also a possibility for the Simpel Desa, a village administration application that also contain and use the Personal Identification Number. This study aims to detect information security vulnerabilities. This study aims to analyze security vulnerabilities in applications using MobSF and ISO 27002:2013. MobSF is used for penetration testing for malware in applications. In MobSF the Simpel Desa application is analyzed in two ways, namely static and dynamic. ISO 27002:2013 is used to map the findings of vulnerabilities and potential misuse of information so that they get accurate analysis results. The control used is domain 9 (access control) and 10 (cryptography). The results obtained in the static analysis found the existence of vulnerabilities in aspects of cryptography and permission access. The dynamic analysis found that Root Detection and Debugger Check Bypass had not been implemented. Overall, based on ISO 27002:2013 information security has not been maximally implemented. The recommendations given focus on the aspects of application permissions and access rights, user authentication, and the implementation of information security.
COMPARISON OF BILSTM, SVM FOR PBB-P2 TAX POLICY SENTIMENT ANALYSIS Rofiqoh, Dayana; Subarkah, Pungkas; Isnaini, Khairunnisak Nur
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 12 No. 2 (2026): Maret 2026
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v12i2.4199

Abstract

Abstract: The policy to increase the Rural and Urban Land and Building Tax (PBB-P2) in Indonesia often elicits mixed reactions from the public. Some support it because they believe it can strengthen regional fiscal capacity, while others reject it because they are concerned that it will increase the economic burden on the community. Understanding public sentiment towards this policy is important for evaluating the effectiveness of the policy and formulating appropriate communication strategies. This study aims to analyze public sentiment towards the PBB-P2 increase policy using data uploaded on Platform X (Twitter). The data were collected through crawling with the keyword “building tax,” then processed through several preprocessing stages before classifying tweets into positive and negative sentiments. Two models were used: Support Vector Machine (SVM) and Bidirectional Long Short-Term Memory (BiLSTM). Results show that SVM outperformed BiLSTM, achieving training accuracy of 99.4% and testing accuracy of 85.9%, with accuracy 0.8595, precision 0.8536, recall 0.8595, and F1-score 0.8449. Meanwhile, BiLSTM achieved training accuracy of 86.9% and testing accuracy of 82.9%, with accuracy 0.8294, precision 0.8150, recall 0.8294, and F1-score 0.8080. These findings suggest SVM is more effective in classifying public sentiment and can support better evaluation of regional tax policies. Keywords: sentiment analysis; PBB-P2; BiLSTM; SVM; X platform Abstrak: Kebijakan kenaikan tarif Pajak Bumi dan Bangunan Perdesaan dan Perkotaan (PBB-P2) di In-donesia sering memunculkan beragam reaksi dari masyarakat. Sebagian mendukung karena dianggap dapat memperkuat kapasitas fiskal daerah, sementara lainnya menolak karena kha-watir menambah beban ekonomi masyarakat. Pemahaman terhadap sentimen publik atas ke-bijakan tersebut penting untuk mengevaluasi efektivitas kebijakan dan merumuskan strategi komunikasi yang tepat. Penelitian ini bertujuan menganalisis sentimen masyarakat terhadap kebijakan kenaikan PBB-P2 menggunakan data unggahan di Platform X (Twitter). Data dik-umpulkan melalui proses crawling dengan kata kunci “pajak bangunan” kemudian diproses melalui beberapa tahap preprocessing sebelum diklasifikasikan menjadi sentimen positif dan negatif. Dua model digunakan dalam penelitian ini, yaitu Support Vector Machine (SVM) dan Bidirectional Long Short-Term Memory (BiLSTM). Hasil penelitian menunjukkan bahwa SVM memiliki kinerja lebih baik dibandingkan BiLSTM, dengan akurasi pelatihan 99,4% dan akurasi pengujian 85,9%. Nilai akurasi 0,8595, precision 0,8536, recall 0,8595, dan F1-score 0,8449. Sementara itu, BiLSTM memperoleh akurasi pelatihan 86,9% dan akurasi pengujian 82,9%, dengan akurasi 0,8294, precision 0,8150; recall 0,8294; dan F1-score 0,8080. Temuan ini menunjukkan bahwa SVM lebih efektif dalam mengklasifikasikan sentimen publik serta dapat mendukung evaluasi kebijakan pajak daerah dengan lebih baik. Kata kunci: analisis sentimen; PBB-P2; BiLSTM; SVM; platform X
Co-Authors Afandi, Marcel Arsi, Primandani Didit Suhartono Ikhsan, Ali Nur Imam Tahyudin Jamil, Muhammad Thoriq Khoirunnita, Anisaa Qothrunnada Kusuma, Bagus Adhi Mutiara, Dwi Ayu Primukti, Aditya Pungkas Subarkah Putra, Bernardus Septian Cahya Putri Kartika Sari Rofiqoh, Dayana Saputri, Inka Senduk, Fabianus Kevin Siti Alvi Solikhatin Waluyo, Retno