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All Journal PENA SAINS Manajemen Pendidikan Haluan Sastra Budaya SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Journal on Education Jurnal Eduscience (JES) Akrab Juara : Jurnal Ilmu-ilmu Sosial BINOMIAL : Jurnal Pendidikan Biologi Jurnal Kependidikan: Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran dan Pembelajaran Jurnal Abdi Masyarakat Indonesia Formosa Journal of Multidisciplinary Research (FJMR) SWARNA JOINCS (Journal of Informatics, Network, and Computer Science)
Riwa Rambu Hada Endah
Universitas Kristen Wira Wacana Sumba
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Journal : JOINCS (Journal of Informatics, Network, and Computer Science)

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Face Detection Using Linear Discriminant Analysis (Lda) Method and Support Vector Machine (Svm) Fajar Hariadi; Riwa Rambu Hada Enda
JOINCS (Journal of Informatics, Network, and Computer Science) Vol 2 No 1 (2019): April
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (914.042 KB) | DOI: 10.21070/joincs.v1i2.521

Abstract

Safety and comfort are basic needs that must be met by all humans. People use CCTV that is often used to monitor public areas that have many people. Initially images from CCTV cameras were only sent via cable to a certain monitor room and needed direct supervision by security personnel with still low image resolution. One method for identifying faces is the Linear Discriminant Analysis (LDA) method. LDA is a method to find a linear subspace that maximizes the separation of two classes of patterns according to Fisher Criterion (fisher criteria weight). This study aims to detect faces with the Linear Discriminant Analysis (LDA) method as extraction features and classify facial images using the Support Vector Machine (SVM) method. The conclusion of this study is that the results obtained from face detection get a fairly high percentage of 84.2% for detected faces and 15.8% for undetectable faces and the results obtained are influenced by a fairly good facial image and image cropping process good and unchanging face position which makes it easy to detect faces.
Comparative Analysis of Text Mining Results With Tf ldf Features and SQL Like Operator in Indonesian News Search Riwa Rambu Hada Enda; Fajar Hariadi
JOINCS (Journal of Informatics, Network, and Computer Science) Vol 3 No 1 (2020): April
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (4619.229 KB) | DOI: 10.21070/joincs.v3i0.714

Abstract

Research on the implementation of text mining uses the TF IDF method to be used in the Information retrieval / Indonesian news search feature. The dataset used was sourced from NewsAPI and built a Codeigniter based website named "News Plus Six Dua". This study also uses the Vertor Space Model (VSM) method to overcome the weaknesses of the TF IDF method at the time of the sorting process. The results of this study explain that the search by the TF IDF method has higher accuracy when compared to SQL like operators. TF IDF produces a percentage of precision 100% and recall (sensitivity) 66.7% on searches with the keyword "Indonesian soccer schedule" while SQL like operators do not display search results or equal to 0%. But the TF IDF method has the disadvantage of running slower than SQL like operators. This has been tested using either the number of words or terms entered, the number of datasets, and the location of access. At the location of access, access via hosting is monitored faster when compared via localhost.
Co-Authors Adrin Lapir M. Meha Agrianto M. Pada Ajeng E.D. Raga Alexander Kaka Anisa Safitri Tanggu Hana Anita Tamu Ina Arifudin Arifudin Citra Rambu Joru Delti Rambu Lunga Erfy Melany Lalupanda Erfy Melany Lalupanda Erfy Melany Lalupanda Erlina Tamo Ina Fajar Hariadi Ferlin K. Ngguna Hernida Hada Inda Isda Bana Ata Siwu Isda M. A. Siwu Jelita Muluana Kalli Karolina Albina Rewa Marianus K. Rihi Marlin F. D. Padji Marten U. D. Palabu Mayun Erawati Nggaba Nefesi Rambu Woji Pekujawang Ospensius Kawawu Taranau Ospensius Taranau Kawawu Taranau Rachel F. Piranyawa Randjawali, Erwin Trivanka Ersiane Eli Valentino M. Rangga Victor Soleman Lawa Lulu Vidriana Oktoviana Bano Vidriana Oktoviana Bano Vidriana Oktoviana Bano Vidriana Oktoviana Bano Vidriana Oktoviana Bano Vidriana Oktoviana Bano Yanti Taba Lokat Yohana Makaborang Yohana Makaborang Yohana Makaborang Yohana Ndjoeroemana Yohana Njdoeroemana Yopiyanus Anamila Yulita Milla Pakereng