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All Journal Sistemasi: Jurnal Sistem Informasi INOVTEK Polbeng - Seri Informatika Building of Informatics, Technology and Science METRIK SERIAL TEKNOLOGI DAN SAINS The Indonesian Journal of Computer Science
Rozanda, Nesdi Evrilyan
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Computer Science & IT Electrical & Electronics Engineering Energy Engineering Physics Other

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Penggunaan Metode FMEA dalam Penilaian Manajemen Risiko Keamanan Sistem Informasi Rumah Sakit Saputri, Setia Ningsih; Salisah, Febi Nur; Maita, Idria; Rozanda, Nesdi Evrilyan
Jurnal Inovtek Polbeng Seri Informatika Vol 9, No 1 (2024)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/isi.v9i1.3951

Abstract

Untuk mengurangi risiko yang terkait dengan suatu organisasi, identifikasi dan pengendalian risiko disebut manajemen risiko. Tujuan manajemen risiko adalah untuk melindungi dan memperkecil setiap kegagalan keamanan sistem informasi perusahaan dari tingkat risiko yang paling tinggi sehingga tidak dapat mencapai tujuan perusahaan. Metode FMEA (Failure Mode and Effect Analysis) adalah salah satu metode yang digunakan dalam penilaian dan pengelolaan risiko. Penelitian ini menganalisa risiko pada sistem informasi suatu industri yang bergerak dibidang kesehatan, yaitu RSU Indah Bagan Batu. RSU Indah Bagan Batu menerapkan sistem informasi yang Bernama SIMRS Khanza. Menentukan tingkat risiko yang ada pada sistem informasi RSU Indah Bagan Batu adalah tujuan dalam penelitian ini, karena SIMRS Khanza harus melindungi data pasien, dokter, obat-obatan, dan staf lainnya dari potensi ancaman. Berdasarkan penilaian risiko menggunakan metode FMEA yang dihitung melalui perkalian severity, occurrence, dan detection maka mendapatkan hasil RPN dengan 34 risiko yang terdapat pada sistem informasi yang digunakan. Dari 34 RPN yang dihasilkan terdapat 5 kategori level RPN yaitu 3 kategori very high dengan rentang 500-504, 5 kategori high dengan rentang 120-180, 6 kategori moderate dengan rentang 84-108, 17 kategori low dengan rentang 20-72 dan 4 kategori very low dengan rentang 8-15.
Analisis Kepuasan Pengguna Akhir Aplikasi Mytelkomsel Menggunakan Metode End User Computing Satisfaction (EUCS) Anahyu, Yelfi Dwi; Zarnelly, Zarnelly; Rozanda, Nesdi Evrilyan; Megawati, Megawati
Jurnal Inovtek Polbeng Seri Informatika Vol 9, No 1 (2024)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/isi.v9i1.3998

Abstract

Aplikasi MyTelkomsel merupakan layanan yang diciptakan Telkomsel berbentuk aplikasi sehingga memberikan kemudahan kepada pengguna dalam mengelola akun dan mengakses layanan menggunakan smartphone. Tujuan penelitian ini adalah untuk menilai pengaruh variabel isi, keakuratan, bentuk, kemudahan pengguna, dan ketepatan waktu terhadap user satisfaction (kepuasan pengguna) aplikasi Mytelkomsel, dengan harapan hasilnya dapat meningkatkan user satisfaction di masa mendatang. Penelitian ini menggunakan metode End User Computing Satisfaction (EUCS) dengan lima variabel bebas : isi, keakuratan, bentuk, kemudahan pengguna, ketepatan waktu, serta satu variabel terikat user satisfaction. untuk proses pengumpulan data sebagai bahan penelitian maka dilakukan penyebaran kusioner secara online kepada pengguna aktif aplikasi Mytelkomsel versi terbaru dan data diolah menggunakan perangkat lunak SPSS 26. Hasil penelitian menunjukkan bahwa dari uji T parsial, setiap dari lima variabel, yaitu Variabel isi, keakuratan, bentuk, kemudahan pengguna, ketepatan waktu, menunjukkan pengaruh yang significant terhadap user satisfaction, sehingga hipotesis H1, H2, H3, H4, dan H5 dapat diterima. Hasil uji F juga mengindikasikan bahwa secara bersama-sama, Isi, keakuratan, bentuk, kemudahan pengguna, ketepatan waktu mempengaruhi user satisfaction aplikasi Mytelkomsel, sehingga H6 diterima. Variabel independen yang paling dominan memengaruhi user satisfaction adalah Format sebab memiliki hasil koefisien regresi tertinggi pada analisa regresi linier berganda.
Sistem Pakar Diagnosa Penyakit Ikan Baung Berbasis Mobile Menggunakan Teorema Bayes Rozanda, Nesdi Evrilyan; Wardhana, Febri
Metrik Serial Teknologi dan Sains Vol. 3 No. 1 (2022)
Publisher : Konsorsium Cendekiawan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51616/teksi.v3i1.275

Abstract

Pada umumnya banyak peternak ikan baung yang tidak mengetahui penyakit yang menyerang ikan penyakit yang terdapat pada ikan tersebut. Oleh sebab itu penelitian ini telah membuat sistem pakar diagnosa ikan baung, sehingga dapat membantu para peternak ikan baung dalam mendiagnosa awal mereka. Selain itu karena kurangnya pengetahuan mereka dan juga sulitnya mendapat konsultasi dengan pakar dibidang tersebut menjadi salah satu penyebabnya. Kondisi inilah yang membuat para peternak ikan baung mengabaikan penyakit yang diderita ikan baung. Sistem pakar dibangun berbasis android, dengan menerapkan Metode Teorema Bayes dalam menampilkan hasil diagnosa awal. Berdasarkan akusisi 2 orang pakar, didapatkan data 5 jenis penyakit, dan 33 gejala penyakit yang menyertainya. Dalam pengujian menggunakan 3 metode pengujian yaitu blackbox testing, uji validasi data, dan UAT. Hasil pengujian blackbox testing menunjukan fitur pada tiap sistem berfungsi dengan baik sebesar 100%. Hasil pengujian validasi data menunjukan tingkat kesamaan 100%. Hasil pengujian UAT menunjukkan tingkat penerimaan sebesar 97% dari 5 orang responden.
ANALISIS EFEKTIVITAS SISTEM INFORMASI AKADEMIK MENGGUNAKAN METODE DELONE AND MCLEAN Rozanda, Nesdi Evrilyan; Razmi, Fikri; Zarnelly, Zarnelly; Megawati , Megawati
Metrik Serial Teknologi dan Sains Vol. 5 No. 1 (2024): Februari 2024
Publisher : Konsorsium Cendekiawan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51616/teksi.v5i1.510

Abstract

Institut Agama Islam Lukman Edy (IAILE) Pekanbaru telah menerapkan sistem informasi akademik (Siakad). Dalam hal ini, IAILE berusaha untuk meningkatkan efisiensi dan efektivitas namun masih banyak kendal-kendala pengguna dalam mengeakses siakad. Tujuan penelitian ini adalah menganalisis efektivitas sistem informasi akademik di Institut Agama Islam Lukman Edy. Metodologi penelitian yang digunakan metodologi Delone & Mclean. Kuesioner yang disusun dan disebarkan kepada 100 responden yaitu yang tercakup sebagai pegawai, dosen, dan mahasiswa di Institut Agama Islam Lukman Edy. Uji validitas dan reliabilitas terhadap data penelitian dilakukan untuk memperoleh data yang valid dan reliabel. Pada Penelitian ini diajukan 9 Hipotesis. Selanjutnya diolah menggunakan metode PLS-SEM dengan software smartPLS. Hasil penelitian menunjukkan 5 hipotesis yang tidak berpengaruh dan 4 hipotesis yang berpengaruh terhadap efektivitas sistem informasi Akademik Institut Agama Islam Lukman Edy (IAILE) Pekanbaru.
Perbandingan Algoritma Support Vector Machine dan Naïve Bayes dalam Menganalisis Sentimen Pinjaman Online di Twitter Ikhsani, Yulia; Permana, Inggih; Salisah, Pebi Nur; Mustakim, Mustakim; Rozanda, Nesdi Evrilyan
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6106

Abstract

Unemployment is one of the poverty factors in society, the large economic needs make it difficult for people to meet their daily needs, thus triggering high demand for loans in society. With the advancement of technology, online loans are now available to help people meet their economic needs. However, over time, many irresponsible parties have taken advantage of this. Marked by the emergence of many illegal online loans, which have triggered negative impacts such as the spread of customer personal data, terror on social media, to debt collection using debt collectors. So that it raises a lot of sentiment in society regarding online loans. For this reason, it is necessary to conduct a sentiment analysis with the aim of public response to online loans, which can be positive, negative or neutral responses. There are two datasets used, namely legal online loans and illegal online loans. This study uses two algorithms, namely SVM and Naive Bayes, the two algorithms will be compared to find out which algorithm is better at analyzing online loan sentiment. In addition, in its use, the two algorithms will also use the SMOTE technique to stabilize the data. The results obtained on legal loan data classification using SVM are quite better than Naive Bayes, with an accuracy rate of 69% with sentiment that often appears is positive sentiment. For illegal loan data, classification using the Naive Bayes algorithm is better than SVM with an accuracy of 75% and sentiment that often appears is neutral sentiment. Based on these results, it can be concluded that in analyzing sentiment using legal loan data, the best algorithm is the SVM algorithm, and for illegal loan data, the best algorithm is the Naive Bayes algorithm.
Evaluasi Tingkat Kapabilitas Teknologi Informasi pada STAI Auliaurrasyidin Tembilahan Menggunakan Farmework COBIT 2019 Ansyari, Muhammad Fadli; Megawati; Saputra, Eki; Rozanda, Nesdi Evrilyan
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4553

Abstract

Information Technology (IT) plays a vital role in supporting operational efficiency and innovation, particularly in higher education. XYZ University has implemented the Integrated Academic Information System (AKSIA) to manage academic data and student services. However, technical challenges in system management require further evaluation using the COBIT 2019 framework. The evaluation focuses on DSS01 (Managed Operations), APO04 (Managed Innovation), and APO09 (Managed Service Agreements) domains, as determined through design factor analysis. Based on interviews, observations, and questionnaires analyzed using the Guttman scale and gap analysis, it was found that all domains are at Capability Level 1 with largely achieved performance: DSS01 at 75%, APO04 at 60%, and APO09 at 66%. The recommendations include improving process documentation, conducting routine monitoring of activities, and aligning services more closely with strategic needs. These steps are expected to elevate IT capability to higher levels and better support the institution's strategic objectives.
Analisis Sentimen Terhadap Program Makan Bergizi Gratis Menggunakan Algoritma Machine Learning Pada Sosial Media X Triningsih, Elsa; Afdal, M; Permana, Inggih; Rozanda, Nesdi Evrilyan
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6534

Abstract

The government has launched the Free Nutritious Meal Program as part of a strategic effort to reduce stunting in Indonesia. However, the program has generated a lot of controversy among the public, especially regarding the large budget allocation that is considered burdensome and its impact on the education sector and the country's financial stability. This study aims to analyze public sentiment towards the program by utilizing data from social media platform X (Twitter) as much as 2,400 data. Public sentiment is classified into three categories, namely positive, negative, and neutral, using two machine learning algorithms, namely Support Vector Machine (SVM) and Random Forest. In addition, the SMOTE technique is used to handle data imbalance in the model training process. The analysis results showed that negative sentiments dominated at 46%, with the main issue highlighted being the high budget allocation and its impact on education. In terms of performance, the SVM algorithm with SMOTE produced the highest accuracy of 85.74%, outperforming the Random Forest algorithm which only achieved 81.53% accuracy.
Risk Analysis of the Information System of the Riau Provincial Plantation Agency Website using ISO 31000 Fernanda, Ustara Dwi; wati, Mega; Rozanda, Nesdi Evrilyan; Salisah, Febi Nur
SISTEMASI Vol 14, No 4 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i4.5368

Abstract

The website of the Riau Provincial Plantation Agency plays a vital role in supporting public information and administrative services. However, the use of information technology also introduces various risks that may disrupt system operations. This study aims to analyze information technology risks associated with the website using the ISO 31000:2018 risk management framework. A qualitative descriptive approach was employed, utilizing interviews, observations, and documentation. The risk management process was conducted through the stages of risk identification, analysis, evaluation, treatment, as well as monitoring and review. The findings identified nine main risks. Eight of them were categorized as medium-level risks, including lightning, fire, human error, data corruption, server downtime, hardware damage, overheating, and power outages. One risk—software updates—was classified as low-level. This study is limited to information technology risks identified internally, based on primary data collected from the website management team. The findings provide risk mitigation recommendations that can serve as guidelines to enhance the security and continuity of the information system within the Riau Provincial Plantation Agency.
Analisis Sentimen Masyarakat Terhadap Kebocoran Pusat Data Nasional Sementara Menggunakan Algoritma Random Forest dan Support Vector Machine Basri, Faishal Khairi; Afdal, M; Angraini, Angraini; Rozanda, Nesdi Evrilyan
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7473

Abstract

A ransomware attack on Indonesia’s Temporary National Data Center (PDNS) in June 2024 triggered major public concern over data security and government preparedness. This study aims to analyze public sentiment toward the incident using an Aspect-Based Sentiment Analysis approach on 2,700 Indonesian-language tweets collected from the X platform. The research follows the SEMMA (Sample, Explore, Modify, Model, Assess) methodology, involving text preprocessing, aspect extraction using part-of-speech tagging and named entity recognition, feature representation using Term Frequency-Inverse Document Frequency, and aspect refinement through semantic coherence. Extracted aspects are grouped into five categories: data security, institutions, infrastructure, politics and economy, and impact. Sentiment classification is carried out using the IndoBERTweet model. Results indicate a strong dominance of negative sentiment, particularly in the infrastructure and institutional categories, with no positive sentiment recorded in the political and economic aspect. To address class imbalance in sentiment distribution, the Synthetic Minority Oversampling Technique is applied during model training. Performance evaluation of two algorithms—Random Forest and Support Vector Machine—shows that Random Forest performs best, achieving 96% accuracy on a 70:30 data split and 99.05% average accuracy using 10-fold cross-validation. These findings highlight the effectiveness of aspect-based sentiment analysis and demonstrate Random Forest's superiority in handling imbalanced sentiment classification tasks.
Co-Authors Anahyu, Yelfi Dwi Angraini Angraini Ansyari, Muhammad Fadli Basri, Faishal Khairi Eki Saputra Febi Nur Salisah, Febi Nur Fernanda, Ustara Dwi Ikhsani, Yulia Inggih Permana M Afdal Mega wati, Mega Megawati Megawati , Megawati Megawati - Mustakim Mustakim Razmi, Fikri Salisah, Pebi Nur Saputri, Setia Ningsih Triningsih, Elsa Wardhana, Febri Zarnelly Zarnelly