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Penerapan Network Attached Storage Menggunakan Openwrt Studi Kasus: Bagian Kemahasiswaan STIKOM Bali Dharmendra, I Komang; Desiani, Luh Putu Ayu
Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) 2015
Publisher : Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I)

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Abstract

Bagian Kemahasiswaan merupakan salah satu departemen di STIKOM bali yang menangani segala seasuatu yang terkait dengan mahasiswa, dengan banyaknya aktifitas yang dilakukan mengakibatkan banyaknya data yang harus disimpan dan diakses setiap harinya. Dengan menggunakan fasilitas file sharing yang dibuka pada salah satu komputer pengguna telah membantu memenuhi kebutuhan akan media penyimpanan terpisah, namun untuk mengkases berkas pada komputer tersebut harus ketika komputer tersebut menyala dan dengan username yang dimiliki oleh komputer tersebut. Dengan menggunakan penyimpanan data terpusat pada NAS dan memanfaatkan OpenWRT sebagai server. Diharapkan data yang disimpan menjadi lebih mudah diakses dan dengan penggunaan daya listrik yang lebih minimal.
Analisa Sentiment Untuk Opini Alumni Perguruan Tinggi komang dharmendra; Komang Oka Saputra; I Nyoman Pramaita
Jurnal Teknologi Elektro Vol 18 No 2 (2019): (Mei-Agustus) Majalah Ilmiah Teknologi Elektro
Publisher : Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (483.896 KB) | DOI: 10.24843/MITE.2019.v18i02.P11

Abstract

Opinion is one of the most important parts in decision making, in processing opinions require a thorough analysis process. Especially text-based opinion, where opinion in the form of opinions do not have a definite value limit for the input. Sentiment Analysis as a branch of knowledge from Text mining can be applied in the opinion analysis process in the form of text. Where opinions will be classified into 3 types of opinions, namely positive opinions, neutral opinions and negative opinions. This study grouped opinions from university graduated students using the SVM and NBC algorithms which in this study were divided into 3 main components, namely the input component, opinion grouping system, and output components.Opinion to be processed is data in the form of a * .csv format opinion file, which then conducts a grouping of opinions. Then the system produces output in the form of 3 types of opinions, namely, positive opinions, neutral opinions and negative opinions. In general, the accuracy results show the differences in the accuracy of each sentiment. From the test results generally shows the accuracy with the highest accuracy value in the NBC algorithm reaching 94.45 while the highest accuracy rate in the SVM algorithm reaches 75.76%.
Klasifikasi Penerima Bantuan Kredit Koperasi Dengan Metode ID3 Ida Bagus Suradarma; I Komang Dharmendra
Jurnal Sistem dan Informatika (JSI) Vol 11 No 1 (2016)
Publisher : Bagian Perpustakaan dan Publikasi Ilmiah - Institut Teknologi dan Bisnis (ITB) STIKOM Bali

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Abstract

Koperasi adalah badan hukum yang berdasarkan atas asas kekeluargaan yang anggotanya terdiri dari orang perorangan atau badan hukum sdengan tujuan untuk mensejahterakan anggotanya. Khusus untuk penyaluran kredit biasanya koperasi masih menggunakan cara yang manual untuk penentuan diijinkan atau tidaknya pemberian kredit. Dan proses ini membutuhkan waktu yang lama, yakni meminta persetujuan pada atasan, memeriksa jaminan dan yang lainnya. Salah satu solusi yang dapat dilakukan adalah membuat sebuah sistem untuk melakukan klasifikasi berdasarkan data histori yang sudah ada, yakni dengan menggunakan metode ID3. Algoritma ID3 membentuk pohon keputusan dari beberapa data simbolik yang bersifat tetap ataupun historikal untuk melakukan pembelajaran mengklasifikasikan data tersebut dan memprediksi klasifikasi data yang baru. Metode ini menerima empat buah inputan yaitu penghasilan, status pernikahan, pekerjaan, dan kepemilikan asset. Sedangkan untuk output dari metode ini adalah diterima atau ditolak pengajuan kreditnya.
Implementasi keamanan informasi file dokumen shipping menggunakan algoritma AES Shipping I Komang Dharmendra; Ni Nym Utami Januhari; I Putu Ramayasa; I Made Agus Wirahadi Putra
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 7 No. 1 : Tahun 2022
Publisher : LPPM UNIKA Santo Thomas

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Abstract

Opinion is an important part of decision making, so it takes the ability to get information from opinions. Sentiment Analysis is a branch of science from Text mining that can be used for opinion analysis in the form of text to classify opinions into 3 types of opinions, namely positive opinions, neutral opinions and negative opinions. Support Vector Machine (SVM) is one method that is widely applied for text mining because it is able to show good performance (Styawati and Mustofa, 2019). SVM works with a learning system that uses a hypothetical space in the form of linear functions in a high-dimensional feature space. Maximum Entropy is a probabilistic classification algorithm that belongs to the class of exponential models, which is based on the principle of Maximum Entropy. Maximum Entropy can be used to solve text classification problems such as Language detection, topic classification, and sentiment analysis. Sentiment analysis was tested using the Support Vector Machine (SVM) and Maximum Entropy methods to test the accuracy of each method in analyzing the sentiments of college alumni opinions. from the test results show Maximum Entropy has a better level of accuracy with the results of 95.45%
Implementasi keamanan informasi file dokumen shipping menggunakan algoritma AES Shipping I Komang Dharmendra; Ni Nym Utami Januhari; I Putu Ramayasa; I Made Agus Wirahadi Putra
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 7 No. 1 : Tahun 2022
Publisher : LPPM UNIKA Santo Thomas

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Abstract

Opinion is an important part of decision making, so it takes the ability to get information from opinions. Sentiment Analysis is a branch of science from Text mining that can be used for opinion analysis in the form of text to classify opinions into 3 types of opinions, namely positive opinions, neutral opinions and negative opinions. Support Vector Machine (SVM) is one method that is widely applied for text mining because it is able to show good performance (Styawati and Mustofa, 2019). SVM works with a learning system that uses a hypothetical space in the form of linear functions in a high-dimensional feature space. Maximum Entropy is a probabilistic classification algorithm that belongs to the class of exponential models, which is based on the principle of Maximum Entropy. Maximum Entropy can be used to solve text classification problems such as Language detection, topic classification, and sentiment analysis. Sentiment analysis was tested using the Support Vector Machine (SVM) and Maximum Entropy methods to test the accuracy of each method in analyzing the sentiments of college alumni opinions. from the test results show Maximum Entropy has a better level of accuracy with the results of 95.45%
PELATIHAN PENGEMBANGAN MEDIA PEMBELAJARAN DI SMA N 1 BLAHBATUH I Made Agus Wirahadi Putra; I Komang Dharmendra; Tubagus Mahendra Kusuma
WIDYABHAKTI Jurnal Ilmiah Populer Vol. 4 No. 1 (2021): Nopember
Publisher : STIKOM Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30864/widyabhakti.v4i1.277

Abstract

SMA Negeri 1 Blahbatuh merupakan Sekolah Menengah Atas yang terletak di jalan. Astina Jaya , kecamatan Blahbatuh, Kabupaten Gianyar. SMA N 1 Blabatuh berjarak 22 Km dengan waktu tempuh normal 60 menit dari kampus ITB STIKOM Bali Renon. Masa pandemi covid memaksa sekolah SMA N 1 Blahbatuh untuk melakukan pembelajaran secara online untuk mengurangi interaksi. Pembelajaran online dengan media yang kurang meanrik menyebabakan materi yang disampaikan kurang diminati oleh sisiwa. Melalui pengabdian ini dilakukan pelatihan optimasi moodle dan pembuatan media pembelajaran dengan canva. Pengabdian dilakukan melalui 4 tahapan yaitu sosialisasi, penyusunan modul, pelatihan (online dan offline) dan terakhir adalah evaluasi. Berdasarkan hasil evaluasi adanya peningkatan pemahaman akan membuat media pembelajaran dengan canva.
Comparison of the DBSCAN Algorithm and Affinity Propagation on Business Incubator Tenant Customer Segmentation Dedy Panji Agustino; I Gede Bintang Arya Budaya; I Gede Harsemadi; I Komang Dharmendra; I Made Suandana Astika Pande
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 12, No 2 (2023): JULI
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i2.1682

Abstract

The increasingly complex business environment necessitates businesses to design more effective and efficient strategies for company development, including market expansion. To understand customer behaviors, customer data analysis becomes crucial. One common approach used to group customers is segmentation based on RFM analysis (Recency, Frequency, and Monetary). This study aims to compare the performance of two clustering algorithms, namely DBSCAN and Affinity Propagation (AP), in providing customer profile segment recommendations using RFM analysis. DBSCAN algorithm is employed due to its ability to identify arbitrarily shaped clusters and handle data noise. On the other hand, Affinity Propagation (AP) algorithm is chosen for its capability to discover cluster centers without requiring a pre-defined number of clusters. The transaction dataset used in this research is obtained from one of the business incubator tenants at STIKOM Bali. The dataset undergoes preprocessing steps before being segmented using both DBSCAN and AP algorithms. Performance evaluation of the algorithms is conducted using the Silhouette Scores and Davies-Bouldin Index (DBI) matrices. The research findings indicate that the AP algorithm outperforms DBSCAN in this customer segmentation case. The AP algorithm yields Silhouette Scores of 0.699 and DBI of 0.429, along with recommendations for 4 customer segments. Furthermore, further analysis is performed on the AP results using a statistical approach based on the mean values of each segment for the RFM variables. The four customer segments generated by the AP algorithm, based on the mean values of the RFM variables, can be associated with the concept of customer relationship management.
Operational Optimization through the Development of a Digital Financial Transaction Recording Website in Village Owned Enterprise Sarwada Amerta, Taro Village: Optimalisasi Operasional melalui Pengembangan Website Pencatatan Transaksi Keuangan Digital di BUMDesa Sarwada Amerta Desa Taro Tubagus Mahendra Kusuma; I Gede Bintang Arya Budaya; I Made Suandana Astika Pande; I Komang Dharmendra
Mattawang: Jurnal Pengabdian Masyarakat Vol. 4 No. 2 (2023)
Publisher : Yayasan Ahmar Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.mattawang1821

Abstract

In this community services activity, mentoring and implementation of a digital financial transaction recording system were conducted in village owned enterprise (VOE) Sarwada Amerta, Taro Village. The findings of this study demonstrate that the utilization of financial transaction information systems can optimize the financial operational processes of VOE. The efficiency and accuracy of financial data processing have improved, and the accessibility of financial information has become easier. However, it was found that the adaptation to the new information system still requires time for the human resources to consistently utilize the system. Strong support and commitment from relevant stakeholders, along with continuous educational efforts, are crucial factors in ensuring the successful implementation of the information system. This community services activity is expected to contribute to the economic development of the village through financial management education for VOE and by encouraging the utilization of information technology in local business financial management. Abstrak Pada kegiatan pengabdian masyarakat ini, dilakukan pendampingan dan implementasi sistem pencatatan keuangan digital di BUMDesa Sarwada Amerta, Desa Wisata Taro. Berdasarkan hasil dari kegiatan ini menunjukkan bahwa penggunaan sistem informasi pencatatan transaksi keuangan dapat mengoptimalkan proses operasional BUMDesa, khususnya dalam bidang keuangan. Efisiensi dan akurasi dalam pengolahan data keuangan meningkat, serta aksesibilitas informasi keuangan menjadi lebih mudah. Namun, ditemukan bahwa adaptasi terhadap sistem informasi yang baru masih memerlukan waktu bagi sumber daya manusia BUMDesa agar dapat menggunakan sistem dengan konsisten. Dukungan dan komitmen yang kuat dari pihak terkait, serta upaya edukasi yang berkelanjutan, menjadi faktor penting dalam memastikan keberhasilan implementasi sistem informasi ini. Kegiatan pengabdian ini diharapkan memberikan kontribusi dalam pengembangan ekonomi desa dengan edukasi manajemen keuangan BUMDesa serta mendorong pemanfaatan teknologi informasi dalam pengelolaan keuangan usaha di tingkat lokal.
IMPLEMENTASI TEXT MINING UNTUK KLASIFIKASI OPINI ALUMNI PADA PERGURUAN TINGGI I Komang Dharmendra; I Gusti Ngurah Ady Kusuma; Ida Ayu Mirah Cahya Dewi; Edwar
Jurnal Teknologi Informasi dan Komputer Vol. 9 No. 3 (2023): Jurnal Teknologi Informasi dan Komputer
Publisher : LPPM Universitas Dhyana Pura

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Abstract

Penelitian bertujuan untuk menganalisis sentimen opini alumni menggunakan metode Support Vector Machine (SVM). Analisis sentimen opini alumni merupakan faktor penting dalam evaluasi kualitas institusi pendidikan. Metode SVM digunakan untuk mengklasifikasikan opini dengan tingkat keakuratan yang tinggi, dengan menggunakan TF-IDF untuk pembobotan dan vektorisasi. Hasil penelitian menunjukkan akurasi sebesar 0.873, dengan nilai precision, recall, dan F1-Score berturut-turut sebesar 0.877, 0.803, dan 0.823. Temuan ini menunjukkan bahwa SVM dapat menjadi pilihan yang efektif dalam analisis sentimen opini alumni. Hasil penelitian memberikan wawasan penting bagi institusi pendidikan untuk memahami dan meningkatkan kepuasan alumni, serta mengidentifikasi aspek-aspek yang perlu diperbaiki. Dalam konteks pengambilan keputusan, hasil analisis sentimen opini alumni dapat memengaruhi strategi dan pengembangan program pendidikan. Penelitian selanjutnya dapat mempertimbangkan perluasan sampel dan eksplorasi teknik pemrosesan bahasa alami lainnya untuk meningkatkan performa analisis sentimen opini alumni.
Perbandingan Metode Seleksi Fitur Pada Analisis Sentimen (Studi Kasus Opini PILKADA DKI 2017) Edwar Edwar; I Gusti Agung Ngurah Rai Semadi; Muhamad Samsudin; I Komang Dharmendra
INFORMATICS FOR EDUCATORS AND PROFESSIONAL : Journal of Informatics Vol 8 No 1 (2023): INFORMATICS FOR EDUCATORS AND PROFESSIONAL : JOURNAL OF INFORMATICS (Edisi Khusus
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/itbi.v8i1.2408

Abstract

In sentiment analysis, feature selection is a crucial step as it improves the performance and efficiency of sentiment analysis models. Feature selection also helps reduce the complexity of data dimensions, enabling faster and more efficient analysis. However, selecting relevant features poses a challenge as choosing the wrong features can decrease the accuracy of the constructed models. In this study, sentiment analysis was conducted on tweet data from the 2017 Jakarta gubernatorial election using TF-IDF feature selection combined with Recursive Feature Elimination (RFE), Chi Square, and Mutual Information. The models were evaluated using Naïve Bayes Classification (NBC) and Support Vector Machine (SVM) algorithms. Evaluation metrics such as accuracy, precision, recall, and F1-Score were used. The experimental results showed that the TfidfVectorizer + RFE combination in the NBC model achieved the highest accuracy of 71.1111% and demonstrated significant performance in terms of precision, recall, and F1-Score