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All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Techno.Com: Jurnal Teknologi Informasi Jurnal Buana Informatika CESS (Journal of Computer Engineering, System and Science) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Information Technology and Computer Science JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURIKOM (Jurnal Riset Komputer) Journal of Information Systems and Informatics Tematik : Jurnal Teknologi Informasi Komunikasi Walisongo Journal of Information Technology Jurnal Indonesia : Manajemen Informatika dan Komunikasi DECODE: Jurnal Pendidikan Teknologi Informasi JITSI : Jurnal Ilmiah Teknologi Sistem Informasi MATRIX : JURNAL MANAJEMEN TEKNOLOGI DAN INFORMATIKA Jurnal Ilmiah Teknologi dan Komputer (JITTER) Jurnal Indonesia : Manajemen Informatika dan Komunikasi
I Made Agus Dwi Suarjaya
Universitas Udayana
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Other

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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

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Pemetaan Pelanggan dengan LRFM dan Two Stage Clustering untuk Memenuhi Strategi Pengelolaan Ni Putu Viona Viandari; I Made Agus Dwi Suarjaya; I Nyoman Piarsa
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 1 (2022): Februari 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (527.802 KB) | DOI: 10.29207/resti.v6i1.3778

Abstract

Maibus is a company of transportation services located in Bali. Transaction data that is owned has not been managed properly. This results in data accumulation and only as a turnover calculation, so LRFM and clustering methods are needed to assist the calculation and processing data in fulfilling customer management strategies. The research was conducted by collecting and understanding data, preprocessing, applying LRFM (Length,Recency,Frequency,Monetary), normalizing LRFM, evaluating the number of clusters with Davies Bouldin Index (DBI), clustering with K-Means, and analyzing cluster results. The data used is transaction data from January 2017 to December 2018 with a total of 14.292 data. The clustering method with the K-means algorithm helps in mapping customers based on transaction data. DBI was used to determine the optimal number of clusters and LRFM used to test the determination of variables in determining customer behavior and loyalty. The results of testing 7.193 invoice using 5 clusters with DBI value is 0.135. The result of customers in cluster 0,2,4 are new customer groups with the proposed strategy is enforced strategy, while the customer in cluster 1 and 3 are lost customers with the proposed strategy is let-go strategy that refers to the customer value and customer loyalty matrix.
Efektivitas Sniffer Menggunakan Natural Language dalam Pembelajaran Lalu Lintas Jaringan Komputer Putu Adhika Dharmesta; I Made Agus Dwi Suarjaya; I Made Sunia Raharja
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 3 (2020): Juni 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (647.53 KB) | DOI: 10.29207/resti.v4i3.1696

Abstract

Computer networks are currently very active in the development of technology that is around us. Seeing this, of course knowledge of the network will be needed if there is a problem on the network. Scapy is a Python module that allows for sending, sniffing and dissecting a packet on a network. This capability allows users to create an application that can dissect how the workings of a network packet. Researchers will create a protocol traffic learning application on a computer network using Scapy and natural language to convey the results of the ongoing sniffing process. The application uses natural language to convey the translation of the sniffing process. The translation result of the sniffing process by using the natural language of this application is expected to be effective and can facilitate and make users understand and learn about the work process of a network packet. To measure the effectiveness of the use of natural language for the translation of the sniffing process a questionnaire was distributed to students of the SMKN 1 Denpasar school majoring in Computer and Network Engineering. The results of the distribution of the questionnaire were then calculated using a Likert scale and then the results obtained that the original results of the sniffing process got a Likert scale value of 37%. While the results of sniffing that have been translated get a value of 73%. This shows respondent better understands the results that have been translated compared to the original results that have not been translated.
Analysis of Public Sentiment Towards Goverment Efforts to Break the Chain of Covid-19 Transmission in Indonesia Using CNN and Bidirectional LSTM Gusti Agung Mayun Kukuh Jaluwana; Gusti Made Arya Sasmita; I Made Agus Dwi Suarjaya
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 4 (2022): Agustus 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (883.246 KB) | DOI: 10.29207/resti.v6i4.4055

Abstract

COVID-19 is a new disease that has a negatively impacts in Indonesia, so the government is taking several measures to suppress the spread of COVID-19, such as new normal, social distancing, health protocols fines, and COVID-19 vaccination. The government's handling efforts have reaped a variety of negative to positive responses from the public on social media, so this study aims to determine the effectiveness of the government's efforts by analyzing public sentiment using the Deep Learning method with 1,875 training datasets consisting of four types government efforts and taken from various media social. The use of Deep Learning begins with testing several Deep Learning architectures to determine the best architecture for predicting data. The architectures tested include CNN and Bi-LSTM, where from these tests, Bi-LSTM outperforms CNN with the best performance achieving the accuracy of 97.34% and 97.33% for precision, recall, and F1-score. The results of public sentiment analysis show that social distancing efforts are considered the most effective by obtaining the most positive sentiments by 33.93%, while the effort to health protocol fines is considered lacking because it obtains the most negative sentiment of 35.64%, so the government must continue to enforce social distancing and optimize other efforts that are still considered ineffective.
Co-Authors A.A. Ketut Agung Cahyawan W Aditama, I Putu Dede Raditya Adyatma, Putu Nanda Arya Agus Kerta Nugraha, I Wayan Anak Agung Ketut Agung Cahyawan Wiranatha Anak Agung Ketut Agung Cahyawan Wiranatha Apriana, Krisna Astuti, Ni Nyoman Indri Wika Ayu Krisnasari Ni Komang Ayu Wirdiani Ayu Wirdiani Bakkara, Kevin Christopher Bhagaskara, I Made Bagita Bouchra, Ferhati Cahyawan Wiranatha, Anak Agung Ketut Agung Candra, I Putu Wijaya Adi Danito, Philip Datar, Fandy Kusumaraditya Dewa Gede Kesuma Yoga Dextiro, Kadek Deksy Dharmawan, I Putu Yogi Prasetya Diatmika, Nyoman Gede Rayka Sedana Dwi Putra Githa Dwi Rusjayanthi, Dwi Efraim William Solang Eva Martina Sitorus G M Arya Sasmita Gede Widya Dharma Geovaldo, I Putu Hendra Gusti Agung Ayu Putri Gusti Agung Mayun Kukuh Jaluwana I Gusti Ngurah Bagus Picessa Kresna Mandala I Ketut Adi Purnawan I ketut Gede Darma Putra I Made Adhiarta Wikantyasa I Made Sukarsa I Made Sunia Raharja I Made Sunia Raharja, I Made Sunia I Nyoman Piarsa I Putu Agung Bayupati I Putu Agus Eka Pratama I Putu Arya Dharmaadi I Putu Wira Cahaya Pratama Yudha Ida Bagus Gde Dwipermana Sidhi Ida Bagus Kade Taruna Ida Bagus Nyoman Yoga Ligia Prapta Johan Tamin Kadek Suar Wibawa Ketut Mediana Ayu Candrayani Komang Arta Wibawa Krisnadinatha, I Gede Arya Kristina Kristina Luh Kade Devi Dwiyani Made Andika Verdiana Mahadiputra, Putu Gede Krisna Mahaputra, Putu Andre Mahayana, I Putu Gede Panji Badra Nalista, Ni Made Naila Narayana, I Putu Kevin Ari Ngeo Goa, Mario Valentino Ngurah Indra Purnayasa Ni Luh Ketut Inggitarahayu Anggasemara Ni Made Ika Marini Mandenni Ni Putu Ayu Widiari Ni Putu Viona Viandari Novenrodumetasa, Nathania nugraha, gemara adiyasa parahita Nugraha, Made Adhi Satrya Pande Nengah Purnawan Permana, Kadek Arya Putra Prabhaswara, Ilham Yoga Pratama , I Putu Agus Eka Pratama, I Putu Yoga pramesia Purwanthi, Luh Putu Ary Putu Adhika Dharmesta Putu Ratih Wulandari Putu Wira Buana Putu Yudha Yarcana Rahaditya Kusuma, Nyoman Tri Reyhan Todo Noer Yamin Ridho Hisbi Sulaiman Rusjayanthi, Ni Kadek Dwi Sadhaka, Anak Agung Istri Prabhaisvari Salsabila, Archels Ramadhany Saputra, Putu Alta Sari, Ni Kadek Ratna Sasmita, Gusti Made Arya Satriya, Rizki Dwi Savitri, Putu Rheya Ananda Setiawati, Putu Ayulia Shevira, Sheila Solang, Efraim William Susila, A.A Ngurah Hary Trisna , I Nyoman Prayana Trisna, I Nyoman Prayana Vidya Chandradev Wayan Oger Vihikan Wayan Oger Vihikan, Wayan Oger Whurapsari, Gusti Ayu Wahyu Wiartha, I Gusti Made Diva Widia Widhiasih, Ni Putu Nirmala Dewi Widiantari, Ni Putu Triska Wiranatha, A.A. Ketut Agung Cahyawan Wiranatha, Anak Agung Ketut Agung Cahyawan Wiranatha, Anak Agung Ketut Cahyawan Wiratama, Bayu Adhya Yanisa Putri, Komang Sri Zebedeus Cheyso