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All Journal Jurnal Ilmiah Informatika Komputer Jurnal Media Informatika Jurnal Sistem Informasi dan Teknologi Informasi Mars: Jurnal Teknik Mesin, Industri, Elektro dan Ilmu Komputer Design Journal
Yampi R. Kaesmetan
STIKOM Uyelindo Kupang
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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identifikasi Identification of Skin Color Images in Humans Using Principal Component Analysis Method Aurelia Fermina Kambri; Mardiani Bana; Yampi R. Kaesmetan
JUSTINFO | Jurnal Sistem Informasi dan Teknologi Informasi Vol. 1 No. 2 (2023): June 2024
Publisher : LP2M Universitas Widyatama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33197/justinfo.vol1.iss2.2023.1942

Abstract

Skin detection is an important process for classifying human skin color. Some phone cameras produce images in RGB (Red, Green, Blue) format. Various cases of skin detection often perform a transformation from the RGB color space to another color space, such as YCbCr or HSV, to improve detection accuracy. In this journal, we conduct research on skin detection using Teachable Machine, a platform that utilizes machine learning to teach computers to recognize visual patterns. The term "accuracy per epoch" refers to how well the model is at telling which pixels in an image are skin color during each training iteration (epoch) when skin color identification using the Principal Component Analysis (PCA) method on a teachable machine is being done. PCA helps reduce the dimensionality of data, enabling more efficient analysis and increased model accuracy. Teachable Machine makes this process simple by providing a user-friendly interface for training models without requiring in-depth knowledge of code or algorithms. Measuring accuracy per epoch is crucial in the training process because it shows the development of the model's ability to detect human skin over time. We expect the model to improve its accuracy in identifying skin-color pixels with each epoch, thereby enhancing the overall performance of the skin detection system. This research shows the great potential of the teachable machine in skin detection applications, providing a more efficient and accurate solution than traditional methods.  
SISTEM PENGAMBILAN KEPUTUSAN PEMILIHAN VARIETAS TANAMAN TERBAIK UNTUK PERTANIAN LAHAN KERING MENGGUNAKAN METODE PROMETHEE Dewi Fortuna Katemba; Mariano Benediktus Lasa; Max Wiliam Do Lalu; Yampi R. Kaesmetan
Jurnal Ilmiah Informatika Komputer Vol 28, No 3 (2023)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/ik.2023.v28i3.9830

Abstract

Dari Penelitian "Sistem Pengambilan Keputusan Pemilihan Varietas Tanaman Terbaik untuk Pertanian Lahan Kering Menggunakan Metode PROMETHEE" memiliki tujuan untuk mengembangkan sebuah sistem yang dapat membantu dalam pengambilan keputusan dalam memilih varietas tanaman terbaik untuk pertanian di lahan kering. Sistem ini menggunakan metode PROMETHEE sebagai alat bantu untuk menganalisis dan membandingkan berbagai kriteria yang relevan dalam pemilihan varietas tanaman. Metode yang kami gunakan adalah metode PROMETHEE digunakan sebagai alat atau pendekatan untuk melakukan pengambilan keputusan dalam memilih varietas tanaman terbaik untuk pertanian di lahan kering. Hasil penelitian ini memiliki kemampuan untuk menambah alternatif, memilih tipe preferensi dan mengolah data sehingga didapat hasil akhir berupa rangking alternatif komoditi Tanaman yang disarankan untuk ditanam pada suatu lahan kering.
Sistem Pendukung Keputusan Penerimaan Bonus karyawan Menggunakan Metode TOPSIS pada Toko EGO FASION Kupang Greistianti Kobi; Roky Daniel Kopung; Intan Christin Ngik; Yampi R. Kaesmetan
Jurnal Media Informatika Vol. 5 No. 1 (2023): Jurnal Media Informatika
Publisher : Jurnal Media Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jumin.v5i1.2451

Abstract

Sistem Pendukung Keputusan Menentukan karyawan yang mempunyai prioritas tinggi dalam menerima bonus sesuai kriteria yang ditetapkan oleh Toko EGO FASION. Sistem pengambilan keputusan hanya untuk membantu memberikan saran keputusan dengan lebih cepat, akurat, dan mengurangi tingkat kesalahan pada saat pengambilan keputusan. Sistem bonus yang diterima karyawan sebagai strategi perancangan setiap karyawan dapat bekerja secara maksimal. Penerapan metode TOPSIS mempunyai jarak terkecil terhadap solusi ideal positif dang mempunyai jarak terjauh terhadap solusi ideal negatif sehingga menghasilkan solusi ideal berupa nilai preferensi. Tujuan penelitian ini adalah menciptakan sebuah sistem pendukung keputusan yangdapat memberikan solusi ideal untuk penilaian karyawan terkait pemberian bonus dengan mempertimbangkan kriteria perilaku, kehadiran, loyalitas, dan hasil kerja. Hasil penelitian ini menunjukan adanya sistem pendukung keputusan yang dapat mengetahui tingkat kelayakan seorang karyawan dalam menerima bonus. Kriteria yang digunakan adalah perilaku, kehadiran, loyalitas, dan hasil kerja. Sebagai bahan pertimbangan untuk meningkatkan kualitas penilaian karyawan agar dapat memberikan bonus secara baik dengan memperhatikan penentu kriteria dan memberikan kriteria penilaian yang tepat untuk meningkatkan motivasi kinerja karyawan.
Ekstrasi Fitur Dan Kontur Pada Kain Tenun Sabu Menggunakan Metode GLCM (Gray Level Co-occurrence Matrix) Sanrina Natalia Evelin Tolan; Abraham Do Hina; Yampi R. Kaesmetan
Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer Vol. 2 No. 3 (2024): Juni : Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/mars.v2i3.99

Abstract

Sabu woven fabric is one of the cultural heritages of Sabu Island. In addition to being a cultural heritage, Sabu woven fabric is one of the handicrafts that still exist today which is preserved by Sabu women. Based on its manufacture, the classification process of Sabu woven fabric is based on color or motif identification. However, the classification process is not an easy process, because the classification process requires time and experts in the field of Sabu woven fabric. In addition to the classification process, the wider community also does not get much information about Sabu woven fabric clearly, because it is necessary to introduce the type of Sabu woven fabric, so that people can know or recognize the type of Sabu ikat woven fabric based on its type. Digital image processing techniques are utilized to build a system that can overcome the problems faced. Furthermore, image feature extraction will be carried out using gray level co-occurrence matrix (GLCM) with 4 features namely contrast, correlation, energy, and homogeneity with angles of 0°, 45°, 90°, and 135°. Each GLCM feature shows the same value even though the original image is rotated. After image feature extraction, the extracted data will be classified using the TensorFlow library. From these results it can be concluded that the program succeeded in selecting the type of Sabu ikat woven fabric class.
Sistem Pendukung Keputusan untuk Seleksi Siswa Berprestasi di SMA Elpida Noelbaki dengan Metode PROMETHEE Josua Daud Djobo; Aprilya Kana Wadu; Yeri H Djara Djami; Yampi R. Kaesmetan
Design Journal Vol. 2 No. 1 (2024): January
Publisher : Yayasan Pendidikan Mitra Mandiri Aceh (YPMMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58477/dj.v2i1.143

Abstract

This study aims to develop a Decision Support System (DSS) to facilitate the selection of outstanding students at Elpida Noelbaki High School by applying the PROMETHEE method. This method helps in making decisions efficiently and objectively based on several relevant criteria. The research involves multiple stages, including the collection of achievement criteria data, development of the PROMETHEE model, and implementation of SPK in software form. The PROMETHEE method ranks students based on predetermined criteria, helping to identify those deserving recognition. The results are expected to provide positive contributions to the school and decision-makers by offering a strong and transparent basis for the selection process. Additionally, the implementation of SPK aims to enhance the recognition of students' achievements and motivate them to excel further, both academically and non-academically
Co-Authors Abraham Do Hina Aprilya Kana Wadu Aurelia Fermina Kambri Dewi Fortuna Katemba Greistianti Kobi Intan Christin Ngik Josua Daud Djobo Mardiani Bana Mariano Benediktus Lasa Max Wiliam Do Lalu Roky Daniel Kopung Sanrina Natalia Evelin Tolan Yeri H Djara Djami