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All Journal Jurnal Informatika Universitas Pamulang Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal Sistem Komputer & Kecerdasan Buatan KLIK: Kajian Ilmiah Informatika dan Komputer
Ristasari Dwi Septiana
Institut Teknologi Dan Bisnis Swadharma, Jakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Model Prototype Aplikasi Monitoring Tugas Akhir (MonTA) Mahasiswa pada STTI NIIT Ristasari Dwi Septiana; Fajar Septian
Jurnal Informatika Universitas Pamulang Vol 4, No 2 (2019): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (329.448 KB) | DOI: 10.32493/informatika.v4i2.2825

Abstract

Services for students' final tasks are very important to increase the number of graduates to be achieved by Universities. The problem faced is knowing the progress of the students' final task. The objective of this research was to build an application for monitoring the students' final task using a prototype model at STTI NIIT. The Prototype model in this research used to identify what services should be given to users. The collection of user needs is done by using the business model analysis technique to see the business processes that are occurring. Then build the prototype application model based on the requirements analysis. Function testing in this application is done by black-box testing, all of which functions can run well. Prototype this application can monitor the progress of students' final task. This application also can present consulting services for students' final task.
Klasifikasi Anomali Intrusion Detection System (IDS) Menggunakan Algoritma Naïve Bayes Classifier dan Correlation-Based Feature Selection Saipul Anwar; Fajar Septian; Ristasari Dwi Septiana
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol 2, No 4 (2019): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (347.383 KB) | DOI: 10.32493/jtsi.v2i4.3453

Abstract

Intrusion Detection System (IDS) is useful for detecting an attack or disturbance on a network or information system. Anomaly detection is a type of IDS that can detect a deviate attack on the network based on statistical probability. The increasing use of the internet also increases interference or attacks from intruders or crackers that exploit weak internet protocols and application software. When many data packets arrive, a problem arises that needs to be analyzed. The right technique to analyze the data package is data mining. This study aims to classify IDS anomalies using the Naïve Bayes classification algorithm from the results of attribute selection with correlation-based feature selection. This study uses a UNSW-NB15 intrusion detection system data collection consisting of 49 attributes and 321,283 data records. Performance measurements are based on accuracy, precision, F-Measure and ROC Area. The results of attribute selection with correlation-based feature selection leave 4 attributes. The results of the evaluation of IDS anomaly classification using the naïve Bayes algorithm without the precedence of the attributes selected by the correlation technique obtained an accuracy rate of 71.2%. While the classification results if preceded by the attributes selected by the correlation technique obtained an accuracy of 74.8%. Classification with the naïve Bayes algorithm can be improved its accuracy which is preceded by the selection of attributes with correlation techniques.
Analisis Sentimen Vaksinasi Covid-19 Pada Twitter Menggunakan Naive Bayes Classifier Dengan Feature Selection Chi-Squared Statistic dan Particle Swarm Optimization Ristasari Dwi Septiana; Agung Budi Susanto; Tukiyat Tukiyat
Jurnal SISKOM-KB (Sistem Komputer dan Kecerdasan Buatan) Vol. 5 No. 1 (2021): Volume V - Nomor 1 - September 2021
Publisher : Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47970/siskom-kb.v5i1.228

Abstract

Tingginya penyebaran Covid-19 semakin berdampak pada bidang kesehatan, ekonomi, bahkan bidang pendidikan di Indonesia, sehingga pemerintah Indonesia melakukan tindakan vaksinasi Covid-19 guna menekan tingkat penyebaran Covid-19 di Indonesia. Namun hal tersebut dinilai kotroversial sehingga menarik perhatian masyarakat untuk memberikan opini di berbagai media seperti media sosial twitter. Sehingga membutuhkan analisa sentimen masyarakat terhadap upaya pemerintah pada tindakan vaksinasi Covid-19 untuk mencapai hasil prediksi dengan nilai akurasi paling optimal. Proses crawling secara otomatis menggunakan tools Rapidminer akan mengambil data tweets yang mengandung 5 (lima) kata kunci, yaitu “Vaksin Sinovac”, “Vaksin Astrazeneca”, “Vaksin Moderna”, “Vaksin Merah Putih”, dan “Vaksinasi Covid-19”. Dataset tweets didapatkan dari tanggal 4 Agustus 2021 sampai 12 Agustus 2021. Dataset diperoleh sejumlah 2060 tweets dan diberi label secara manual didapatkan jumlah tweet sebanyak 1193 sentimen positif, 73 negatif, dan 794 netral. Data tersebut dianalisa dengan menggunakan Metode Feature Selection Chi-Squared Statistic dan Particle Swarm Optimization (PSO) untuk mengurangi atribut yang kurang relevan pada saat proses klasifikasi dengan algoritma Naive Bayes Classifier (NBC). Hasil pengujian menunjukan bahwa Algoritma Naive Bayes Classifier (NBC) tanpa Feature Selection mendapatkan nilai akurasi 63,69%. Hasil penelitian menunjukkan bahwa Algoritma Naive Bayes Classifier (NBC) dengan Feature Selection Chi-Squared Statistic mempunyai tingkat akurasi 69,13%. Sedangkan hasil pengujian algoritma Naive Bayes Classifier (NBC) dengan Particle Swarm Optimization mempunyai tingkat akurasi 66,02%. Dengan demikian hasil seleksi fitur Chi-Squared Statistic mendapatkan nilai akurasi yang lebih baik jika dibandingkan dengan Particle Swarm Optimization untuk proses klasifikasi algoritma Naive Bayes Classifier (NBC) dengan selisih akurasi 3,11%.
Implementasi Algoritma Greedy dan Algoritma A* Untuk Penentuan Cost Pada Routing Jaringan Ristasari Dwi Septiana; Dimas Abisono Punkastyo; Nurhasan Nugroho
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 3 No. 2 (2022): Oktober 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v3i2.576

Abstract

The current increase in internet development raises new problems in terms of path optimization on the internet. This makes network path optimization a major problem in choosing the shortest route. The purpose of this research is to understand and compare the process of finding the shortest route using two algorithms, namely Greedy and A*. The A* algorithm has an advantage in overcoming network workloads compared to the Greedy algorithm. In implementation, both algorithms have the same results in determining the delivery path. However, the A* algorithm is more effective for use on large and complex networks because it has more certain and accurate calculations. From the test results, it was found that the A* algorithm has better performance than the greedy algorithm in the test. Where the final cost value of the greedy algorithm is 49, while for the A* algorithm is 48
Klasifikasi Anomali Intrusion Detection System (IDS) Menggunakan Algoritma Naïve Bayes Classifier dan Correlation-Based Feature Selection Saipul Anwar; Fajar Septian; Ristasari Dwi Septiana
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 2 No. 4 (2019): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

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

Abstract

Intrusion Detection System (IDS) is useful for detecting an attack or disturbance on a network or information system. Anomaly detection is a type of IDS that can detect a deviate attack on the network based on statistical probability. The increasing use of the internet also increases interference or attacks from intruders or crackers that exploit weak internet protocols and application software. When many data packets arrive, a problem arises that needs to be analyzed. The right technique to analyze the data package is data mining. This study aims to classify IDS anomalies using the Naïve Bayes classification algorithm from the results of attribute selection with correlation-based feature selection. This study uses a UNSW-NB15 intrusion detection system data collection consisting of 49 attributes and 321,283 data records. Performance measurements are based on accuracy, precision, F-Measure and ROC Area. The results of attribute selection with correlation-based feature selection leave 4 attributes. The results of the evaluation of IDS anomaly classification using the naïve Bayes algorithm without the precedence of the attributes selected by the correlation technique obtained an accuracy rate of 71.2%. While the classification results if preceded by the attributes selected by the correlation technique obtained an accuracy of 74.8%. Classification with the naïve Bayes algorithm can be improved its accuracy which is preceded by the selection of attributes with correlation techniques.
Co-Authors Agung Budi Susanto Dimas Abisono Punkastyo Fajar Septian Nurhasan Nugroho Saipul Anwar Tukiyat Tukiyat