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All Journal KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Bulletin of Information System Research
Efori Bu’ulolo
Universitas Budi Darma, Medan
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Sistem Pendukung Keputusan Dalam Menilai Kinerja Tenaga Pendidikan Terbaik Menggunakan Metode WASPAS Ari Pradana; Efori Bu’ulolo
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 5, No 1 (2021): Peran Generasi Milenial Bertalenta Digital Pada Era Society 5.0
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v5i1.3666

Abstract

Tenaga pendidikan merupakan orang yang ahli dalam bidang pendidikan yang mengabdi pada negeri dan di tunjuk sebagai pendukung dalam terselenggarakannya pendidikan yang efektif. Tenaga pendidikan adalah orang yang sangat berperan penting dalam mencerdaskan kehidupan Bangsa Indonesia. Pada penelitian ini, dilakukan proses penilaian kinerja tenaga pendidikan pada Dinas Pendidikan Kota Medan dan diharapkan agar para tenaga pendidikan dapat termotivasi dan lebih bersemangat dalam bertugas serta dapat meningkatkan kinerja para tenaga pendidik agar menjadi lebih baik untuk hari yang akan datang. Kepada para tenaga pendidikan yang terpilih akan mendapatkan penghargaan seperti pemberian reward finansial maupun non-finansial. Tujuan dalam pembuatan penelitian ini adalah untuk membantu dalam proses penyeleksian tenaga pendidikan yang semula dilakukan secara manual dan memakan banyak waktu dalam pengerjaannya yang kemudian beralih menggunakan sistem komputer agar lebih mudah dalam penyelenggaraan penilaian kinerja tenaga pendidikan terbaik pada lingkungan Dinas Pendidikan Kota Medan. Dalam penelitian ini metode yang digunakan dalam menentukan tenaga pendidikan terbaik adalah WASPAS yang merupakan salah satu dari metode pada sistem pendukung keputusan (SPK). Hasil dari penelitian kepada 10 orang yang diseleksi, terdapat tenaga pendidikan dengan alternatif A7 atas nama Dra. Sri Fajar Ningsih, M.Si yang memperoleh predikat terbaik dengan nilai tertinggi yaitu 0,3713
Kombinasi Metode ROC dan OCRA dalam Pemilihan Suplemen Daya Tahan Tubuh Terbaik di Masa Pandemi Covid-19 Khairunnisa Khairunnisa; Efori Bu’ulolo
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 5, No 1 (2021): Peran Generasi Milenial Bertalenta Digital Pada Era Society 5.0
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v5i1.3667

Abstract

Pandemi Covid-19 yang melanda masyarakat sangatlah berakibat bagi keberlangsungan aktifitas bagi masyarakat dimana covid-19 ini menyerang sistem kekebalan tubuh, sehingga untuk mencegahnya haruslah memiliki imunitas tubuh yang baik. Adapun dalam meningkatkan imunitas tubuh ialah dengan mengonsumsi suplemen yang dapat menjaga daya tahan tubuh seperti Nature’s Way, Imboost Force dan lain sebagainya. Banyaknya suplemen daya tahan tubuh yang beredar dipasaran. Sehingga dalam hail ini dibutuhkannya suatu sistem pendukung keputusan dalam menentukan suplemen daya tahan tubuh yang baik di konsumsi dimasa pandemi ini, dalam penentuanya terdapat beberapa alternatif dan kriteria diantaranya Sertifikat dan Izin, Kandungan, Dosis (mg), Kontra Indikasi, dan Harga Per Kemasan. Dibutuhkanlah suatu sistem pendukung keputusan dalam mementukan suplemen daya tahan tubuh terbaik dengan kombinasi metode ROC (Rang Order Centroid) untuk menghasilkan nilai bobot pada suatu kriteria tertentu dan metode OCRA (Operational Competitiveness Rating Analysis) mengasilkan preferensi terbaik berdasarkan data data seperti alternatif dan kriteria yang telah ditentukan, dan menghasilkan nilai preferensi terbaik pada alternatif ke-3 yaitu Nature’s way dengan nilai 0.337
Analisis Perbandingan Kinerja Boldi-Vigna Codes Dengan Algoritma Fixed Lenght Binary Encoding (FLBE) Dalam Kompresi File Text Ewit Purba; Efori Bu’ulolo; Bister Purba
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 6, No 1 (2022): Challenge and Opportunity For Z Generation in Metaverse Era
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v6i1.5700

Abstract

The increasing use of larger data causes problems in data storage, the greater the data stored, the greater the storage space that will be needed. This can cause the data transformation process to be slow and take a long time. Currently, there are many algorithms developed for data compression, but none are so good for compressing various file types because of their different characteristics. One solution or alternative in solving the problem that will be done is to compress the file to reduce the size of the data and speed up the data transmission process so as to save storage space. The algorithms used in this research are Boldi-Vigna (ζ1) Code and Fixed Length Binary Encoding (FLBE) algorithms. To find out the comparison of compression performance, the parameters to be compared are Ratio of Compression (RC), Compression Ratio (CR), Space Saving (SS), Redundancy (Rd), Compression & Decompression Time. Based on the test results show that the Fixed Length Binary Encoding (FLBE) algorithm is better than the Boldi-Vigna (ζ1) Code algorithm where the average result of the comparison of the Boldi-Vigna (ζ1) Code Ratio is 1.69 bits while the Ratio of Compression algorithm Fixed Length Binary Encoding (FLBE) 1.86 bits. The average compression ratio of the Boldi-Vigna algorithm (ζ1) Code is 58.92%, while the Compression Ratio of the Fixed Length Binary Encoding (FLBE) algorithm is 53.57%.
Algoritma Clustering K-Nearest Neighbor Dalam Pengelompokan Masyarakat Kecamatan Medan Area Berdasarkan Tingkat Ekonomi Keluarga Rizky Meliani Astri Hasibuan; Efori Bu’ulolo; Sumiaty Adelina Hutabarat
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 6, No 1 (2022): Challenge and Opportunity For Z Generation in Metaverse Era
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v6i1.5756

Abstract

An economy that is constantly changing and developing may result in increased economic difficulties. Current economic developments have decreased to 4.79, causing poverty in Indonesia to increase. To see the condition of the people's economy, it is necessary to collect detailed data so that people with a lower economy get assistance. The problems faced in this study are data collection and classification of the economic level of the population. The process is not very efficient because it is done manually. Classification procedures will only be carried out when necessary. Controlling the welfare of the population has not been carried out in detail. To overcome these problems, we need a system so that the grouping of people who have a classification from high to low economic level. This study uses the K-NN Clustring Algorithm which requires information training to classify objects that are very close. In the last process the Clustring K-NN algorithm is a method for calculating the ratio of old data to updated information. As well as looking for the highest data occurrence value that will be used as a reference as a result.
Implementasi Algoritma Code-Excited Linear Prediction (Celp) Pada Kompresi File Audio Lucius Yupiter Telaumbanua; Efori Bu’ulolo; Kurnia Ulfa
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 6, No 1 (2022): Challenge and Opportunity For Z Generation in Metaverse Era
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v6i1.5701

Abstract

The use of storage media to store various files, whether text, video or audio, has been implemented for a long time and continues to grow today. The size of a file will affect the data transmission process as well as the use of large enough storage space. This is what lies behind the application of the compression process to a file so that the file size can be reduced and simplify the process of data transmission and use less storage space. One file that is often compressed is an audio file. Audio files often use a large enough storage space when compared to other document files. This is why compression of audio files is needed. In compressing audio files, the author will use the CELP method. This method is part of lossy compression. The lossy compression method recognizes that human hearing is limited so that not all data in the audio file can be heard directly. The lossy algorithm will provide a fairly large change in file size compared to lossless compression. The CELP method is applied because it can perform coding efficiently and the quality is maintained. In this study, it is known that the application of the CELP method on audio files produces an audio file compression application, especially those with the Mp3 extension. The compressed Mp3 audio file has a very small size with a compression ratio of 7.38%.
Analisis Perbandingan Algoritma Lz78 Dengan Algoritma Transformasi Walsh-Hadamard Untuk Kompresi Citra Alexander Pamdapotan Manullang; Efori Bu’ulolo; Meryance Viorentina Siagian
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 6, No 1 (2022): Challenge and Opportunity For Z Generation in Metaverse Era
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v6i1.5792

Abstract

Data communication is an activity that is very often carried out in the field of information technology. Data with a large size will take a longer transfer time compared to data that has a smaller size, sometimes there is a risk that it cannot be accommodated on the storage media and is not delivered, so that it will reduce the empty capacity in the memory of the storage media. This can cause the data transformation process to be slow and take a long time. The solution that can solve the above problem is by doing compression. Compression is a technique that can reduce the size of the image to be smaller than its original size. This compression performs a compression technique with compression properties where it is allowed to lose some or most of the data in the image. The algorithm used in this study is the LZ78 algorithm with the Walsah-Hadamard Transform algorithm. To find out the comparison of the compression performance, the parameters to be compared are Ratio of Compression (RC), Space Saving (SS). Based on the results of the researcher's test in comparing the LZ78 algorithm with the Walsh-Hadamard Transformation, the compression value obtained by calculating the ratio that the Walsh-Hadamard Transform algorithm is better than LZ78 where the results of the comparison of the Ratio of Compression of the Walsh-Hadamard Transformation algorithm are 0.71 bits, while the Ratio of 2.66 bit LZ78 compression algorithm. LZ78 62.5% while the Compression Ratio algorithm Walsh-Hadamard Transformation 76%
Implementasi Algoritma K-Nearest Neighbor(K-NN) Dalam Klasifikasi Kredit Motor Efori Bu’ulolo; Irma Suryani Tampubolon; Christin Vebiola Nababan; Lulu Nurhidayanti Nasution
Bulletin of Information System Research Vol 1 No 1 (2022): Desember 2022
Publisher : Graha Mitra Edukasi

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

Abstract

Determining the eligibility of applying for a motorbike loan to a leasing company is important, considering that if an error in decision making occurs it will have an impact on the loss of the FIF Group company. Therefore the authors created a Decision Support System using the K-nearest Neighbor method to determine the feasibility of applying for a motorcycle loan. The K-Nearest Neighbor (K-NN) algorithm is an algorithm in data mining to classify new objects based on the majority of the nearest neighbor categories. The K-NN clustering algorithm using data on income, employment, number of dependents and home ownership can group prospective new creditors to make it easier for staff to determine acceptance of prospective new motorcycle creditors
Co-Authors Alexander Pamdapotan Manullang Ari Pradana Bister Purba Christin Vebiola Nababan Ewit Purba Hutabarat, Sumiaty Adelina Irma Suryani Tampubolon Khairunnisa Khairunnisa Kurnia Ulfa Lucius Yupiter Telaumbanua Lulu Nurhidayanti Nasution Meryance Viorentina Siagian Rizky Meliani Astri Hasibuan