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All Journal Jurnal Informatika Jurnal Informatika Proceeding International Conference on Information Technology and Business Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) International conference on Information Technology and Business (ICITB) jurnal Teknologi Informasi Magister Sinkron : Jurnal dan Penelitian Teknik Informatika International Journal of Artificial Intelligence Research INTEGER: Journal of Information Technology Jurnal Teknoinfo Jurnal Cendikia CCIT (Creative Communication and Innovative Technology) Journal Building of Informatics, Technology and Science JSR : Jaringan Sistem Informasi Robotik Jurnal Teknik Informatika (JUTIF) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Pengabdian Masyarakat Formosa (JPMF) Jurnal Publika Pengabdian Masyarakat
handoyo widi nugroho
Institut Informatika Dan Bisnis Darmajaya
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Public Health Social Sciences

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Utilization of Content Base Image RetrievalTechnique Based Sketch for Facial Recognition Muhammad Said Hasibuan; Handoyo Widi Nugroho; Suhendro Yusuf
Prosiding International conference on Information Technology and Business (ICITB) 2016: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND BUSINESS (ICITB) 2
Publisher : Proceeding International Conference on Information Technology and Business

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

Abstract

As the rising up Including terrorist crimes in our society due to issues politics, economy, poverty, religion and ethnic conflicts. Many ways and techniques have been tried to crack down Reviews those crimes, but unfortunately the Efforts to seize person or group of suspected criminal is far from our expectation. Face recognition is one of techniques Introduced by many Researchers for the last Decades with many methods and approaches they tried to Recognize a person based on his or his faces. Some of the methods such as face recognition with Query by Example (QBE) using shape, color, and texture to match a query face with the face in the database; however the result is not good enough to Recognize the faces. One of the problems of face recognition by QBE is sometime we do not have a picture or a face image to the make QBE. In order to sort it out the problem, in this research we will try to introduce of face recognition method by generating a face image by a face sketch.Many sketch based face recognition was Introduced by some Researchers and experts, but most of reviews their methods have been applied directly inputting a sketch into a database the which is very costly and Involved a complex algorithm. In addition to the research, we are applying our proposed method compressed into face images, as the compressed images will save storage and unsumming the algorithm. KEY WORDS: Query by Example, Face recognition, criminal 
ANALISA PERBANDINGAN KINERJA ALGORITMA C4.5 DAN ALGORITMA K-NEAREST NEIGHBORS UNTUK KLASIFIKASI PENERIMA BEASISWA Agung Purwanto; Handoyo Widi Nugroho
Jurnal Teknoinfo Vol 17, No 1 (2023): Vol 17, No 1 (2023): JANUARI
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jti.v17i1.2370

Abstract

Penugasan beasiswa adalah masalah manajemen operasi yang dihadapi administrator universitas, yang biasanya diselesaikan berdasarkan pengalaman pribadi administrator. Penelitian ini mengusulkan metode insentif yang terinspirasi oleh pemrograman dinamis untuk menggantikan proses pengambilan keputusan tradisional dalam penugasan beasiswa. Tujuannya adalah untuk menemukan skema penugasan beasiswa yang optimal dengan ekuitas tertinggi sambil memperhitungkan kendala praktis dan persyaratan ekuitas Metodologi yang digunakan dalam menentukan penerima beasiswa di Universitas Muhammdiyah Pringsewu adalah dengan membandingkan tahapan Algoritma C.45 dan Algoritma K-Nearest Neighbors. Dari beberapa data sampel calon penerima dari jurusan Sistem Informasi dan telah dihasilkan berdasarkan perhitungan Algoritma K-Nearest Neighbors memiliki performansi yang lebih baik yaitu presisi 98,72%, akurasi 97,66% dan nilai recall 99,50%, dengan hasil AUC sebesar 0,997 sedangkan C4,5 algoritma. mencapai 98,9% dengan nilai  precision 89,73%, nilai recall 100,00% dan hasil AUC 0,956. Kata Kunci: Beasiswa,Klasifikasi,C4.5, K-Nearest Neighbors
PERBANDINGAN KINERJA ALGORITMA DATAMINING UNTUK PREDIKSI KELULUSAN MAHASIWA Sadimin Sadimin; Handoyo Widi Nugroho
Jurnal Teknoinfo Vol 17, No 2 (2023): Vol 17, No 2 (2023) : JULI
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jti.v17i2.2619

Abstract

Along with the development of technology, especially the development of increasingly large data storage. One organization that has large data storage is an educational organization. Educational organizations use data to obtain information, especially information about students. Student data has many attributes so that we can make predictions such as predictions of student performance, predictions of scholarship recipients and predictions of student graduation. Data mining methods in education are classified into five dimensions, one of which is prediction, such as predicting output values based on input data. From the results of the research conducted from the initial stage to the testing stage of the application of the C4.5 Algorithm, the accuracy results are higher than Naïve Bayes because in its classification stage, C4.5 processes attribute data one by one. The difference is with naïve Bayes which is influenced by the amount of data used, the comparison of the amount of training and testing data. The feasibility of the model obtained is supported by the high accuracy, precision, recall and AUC obtained from the two algorithms that have been tested. The C4.5 algorithm has an accuracy rate of 79.91%, 89.06% precision and 81.38% recall and an AUC value of 0.823. Meanwhile, Naïve Bayes has an accuracy rate of 76.95%, precision of 75.95% and recall of 98.38% and an AUC value of 0.838.
PELATIHAN MICROSOFT OFFICE DAN MEDIA PEMBELAJARAN BERBASIS DIGITAL BAGI SISWA-SISWI SD NEGERI 1 SRIKATON Rini Nurlistiani; Handoyo Widi Nugroho; Andrean Danofic
Jurnal Publika Pengabdian Masyarakat Vol 6, No 1 (2024): Jurnal Publika Pegabdian Masyarakat
Publisher : Institut Informatika dan Bisnis Darmajaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30873/jppm.v6i1.4038

Abstract

The advancement of information and communication technology (ICT) in the industrial era 4.0 which is increasingly developing affects various fields in life, including one of them in the field of education. One of the objects of service carried out is at SD Negeri 1 Srikaton, Lampung. This service is aimed at 6th grade students because many do not understand the importance of information technology as a means of digital-based learning media such as microsoft office and Canva applications. The purpose of this training is to introduce information technology to students for such as office administration and create content marketing on social media media that is interesting and educational. The methodology used in this service is observation, socialization through presentations, demonstrations and direct practice to the participants, the last is mentoring and evaluation. The results of this service are that students are able to create imagination in designing school assignments, both from templates, colors, and font types in the Canva application. As well as the use of basic Office which is quite easy for participants to understand
Implementasi Data Mining Dalam Klasifikasi Tingkat Kesenjangan Kompetensi PNS Menggunakan Metode Naive Bayes Kurniawan, Putra; Wasilah, Wasilah; Sutedi, Sutedi; Nugroho, Handoyo Widi
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5641

Abstract

Civil Servants (Aparatur Sipil Negara or ASN) play crucial roles as implementers of public policy, community service providers, and national unifiers. The government's primary focus is on enhancing the quality and efficiency of public services. In the Provincial Government of Lampung, planning for the enhancement of the competencies of Civil Servants (Aparatur Sipil Negara or ASN) has become a current priority activity. This emphasis is due to the absence of reference data for determining competency development for each ASN. The Assessment Center is one method for determining the competency level of Civil Servants (ASN). However, its implementation faces several challenges such as budget constraints, time limitations, and a shortage of assessors. Based on the results of the 2023 Merit System Index assessment by the Civil Service Commission (KASN), it was recommended that mapping and evaluating employee competency gaps can be carried out through the Human Capital Development Plan (HCDP). In its implementation, a self-assessment method using a questionnaire based on the competency dictionary from the Regulation of the Minister of Administrative and Bureaucratic Reform No. 38 of 2017 is used to address the constraints of the assessment center. The questionnaire is specifically targeted at technical civil servants (PNS) in the Lampung Provincial Government. The analysis of this questionnaire data produces a classification of civil servants based on the level of competency gaps (none, low, medium, high). In this study, the classification results are tested using one of the data mining classification techniques, namely the Naïve Bayes method. The objective of this research is to evaluate the performance of the Naïve Bayes algorithm in classifying the levels of competency gaps among civil servants. Based on the research findings, it can be concluded that the classification system for competency gap levels among civil servants in the Lampung Province Government can be modeled. The testing of the model, which implemented the Naïve Bayes classification method using RapidMiner tools on the research dataset, achieved an accuracy rate of 98.02%. The conclusion is that the Naïve Bayes algorithm performs well in classifying the competency gap levels among civil servants. With the achieved accuracy level, the resulting classifications can be utilized by the Lampung Provincial Government in planning the development needs of civil servant competencies
IMPLEMENTASI SISTEM PENGAMBILAN KEPUTUSAN PEMILIHAN JURUSAN DI SMKN 1 BANDAR LAMPUNG MENGGUNAKAN METODE AHP DAN TOPSIS Nugroho, Handoyo Widi; Susanti, Yus
INTEGER: Journal of Information Technology Vol 9, No 1: Maret 2024
Publisher : Fakultas Teknologi Informasi Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.integer.0.v9i1.5676

Abstract

SMK Negeri 1 Bandar Lampung is a vocational school. Students often choose the wrong major because it does not suit their abilities, this is because they do not know the differences between several majors and there is no more detailed information about majors. With a combination of the AHP and TOPSIS methods, a decision-making system has been implemented in determining majors at SMKN1 Bandar Lampung from several predetermined criteria, namely Report Card Scores, Academic or Non-Academic Achievement Scores and Talent Interest Scores, job opportunities. AHP is used to determine the weight of each criterion. From this research it is known that the highest weight of the criteria resulting from processing with AHP is a Talent Interest Value of 47%. Meanwhile, ranking with TOPSIS resulted in Accounting (AK) being the alternative that had the best alternative value compared to the other alternatives so that it was chosen as the leading major at SMKN1 Bandar Lampung.
Comparison of Data Mining for Classifying Student Graduation Levels Using Naive Bayes, Decision Tree, and Random Forest Methods (Case Study of The Undergraduate Program at Mitra Indonesia University) Destanto, Tri; Nugroho, Handoyo Widi
CCIT (Creative Communication and Innovative Technology) Journal Vol 18 No 1 (2025): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v18i1.3409

Abstract

This study aims to apply data mining techniques to classify student graduation rates in the Undergraduate Program at Mitra Indonesia University. The methods used in this study include Naive Bayes, Decision Tree, and Random Forest. The data used includes student academic data, such as grades, attendance, and other demographic information. The research steps include data collection, data cleaning, data analysis, and the application of data mining algorithms. The results of the study show that the Random Forest method provides the highest accuracy compared to Naive Bayes and Decision Tree in predicting student graduation rates. The Random Forest method achieved an accuracy of 85%, while the Decision Tree achieved 80%, and Naive Bayes achieved 75%. These findings are expected to help Mitra Indonesia University identify students at risk of not graduating on time, so appropriate interventions can be provided to improve graduation rates
MODEL PENGAMBILAN KEPUTUSAN PENERIMA BANTUAN SOSIAL MENGUNAKAN METODE WEIGHTED PRODUCT (WP) DAN TOPSIS DI KAMPUNG PURWAJAYA KECAMATAN BANJAR MARGO TULANG BAWANG Sefriyanto, Eka; Nugroho, Handoyo Widi
Jurnal Cendikia Vol 24 No 2 (2024): Jurnal Cendikia Vol. 24 No. 2 Oktober 2024
Publisher : LPPM AMIK Dian Cipta Cendikia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.2313/jc.v24i1.495

Abstract

This study aims to determine the steps or models of the WP method and the Topsis method and compare the results of the analysis of the two methods, This study aims to determine the steps or models of the WP method and the Topsis method. Low labor absorption and inadequate human resource capabilities make people's lives below the poverty line. Government efforts by launching various types of aid do not make poverty levels reduced. Some contributing factors include the provision of assistance that is not on target and the criteria used as a basis for assessments are not maximal. he selection of methods must be appropriate to anticipate errors in the data to be used. The Weighted Product (WP) method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) part of Multi-Attribute Decision Making (MADM) are used to rank all alternatives of predetermined criteria and sub-criteria. The implementation of the two methods for the ten alternatives (Purwajaya village, Banjar Margo sub-district, Tulang Bawang district) turned out to give very good results. the calculation results of these two methods are different then it is concluded that the best results are Topsis with a value: 0.070137683.
ANALISIS KUALITAS WEBSITE SISTEM INFORMASI AKADEMIK (SIAKAD) ITBA DIAN CIPTA CENDIKIA MENGGUNAKAN METODE WEBQUAL 4.0 (STUDI KASUS SIAKAD ITBA DIAN CIPTA CENDIKIA) Alexander, Ganesis; Nugroho, Handoyo Widi
Jurnal Cendikia Vol 24 No 2 (2024): Jurnal Cendikia Vol. 24 No. 2 Oktober 2024
Publisher : LPPM AMIK Dian Cipta Cendikia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.2313/jc.v24i2.499

Abstract

Kualitas website sistem informasi akan menjadi salah satu gambaran kualitas dari perguruan tinggi. Sedangkan saat ini pihak kampus belum melakukan analisa terhadap kualitas website, sehingga belum diketahui sejauh mana tingkat kualitas dari website yang ada saat ini. Untuk mengukur tingkat kualitas website menggunakan metode yang digunakan dalam penelitian ini adalah Webqual 4.0. Kemudian Indeks Kepuasan Pengguna (IKP) yang terlebih dahulu kuesioner yang ada diuji dengan uji validitas dan reliabilitas, kemudian analisa menggunakan Importance Performance Analysis (IPA) yang digunakan untuk membagi atribut kuesioner kedalam 4 kuadran diagram kartesius yang masing-masing memiliki prioritas untuk pengembangan website. Pada penelitian ini dilakukan uji validitas, dimana semua atribut kuesioner dinyatakan valid karena r Hitung lebih besar dari r Tabel, kemudian uji reliabilitas dengan menggunakan Cronbach’s Alpha dinyatakan bahwa atribut kuesioner kesemuanya reliable karena hasil hitung lebih besar dari 0,6. Dengan analisa Webqual Index diperoleh indeks kualitas website sebesar 0,64 dimana semakin mendekati nilai 1 semakin baik, dan Indeks Kepuasan Pengguna berbanding lurus dengan indeks kualitas website yaitu sebesar 0,64. Hasil analisa metode Importance Performance Analysis menyatakan 5 atribut pada kuesioner yang masuk ke dalam kuadran A yang menjadi prioritas dalam pengembangan website kedepannya.
A Prediksi Rekomendasi Pemilihan Kejuruan pada Sekolah Menengah Kejuruan Menggunakan Perbandingan Metode Decision Tree C4.5 dan Naïve Bayes Windari, Ratih; Nugroho, Handoyo Widi
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6928

Abstract

SMK Negeri 4 Bandar Lampung faces challenges in assisting students in selecting a major that aligns with their potential, interests, and abilities. The decision-making process for choosing a major is often influenced by subjective factors that lack transparency and may not be entirely accurate. Therefore, a system is needed to provide more accurate and objective recommendations. This study develops a predictive system for major selection at SMK Negeri 4 Bandar Lampung using two methods: the Decision Tree C4.5 algorithm and the Naïve Bayes algorithm. The system utilizes seven key attributes as predictive variables, including mathematics scores, English scores, science (IPA) scores, Indonesian language scores, academic achievements, participation in extracurricular activities, and color blindness condition. The study findings indicate that the C4.5 algorithm achieves an accuracy of 84.46%, whereas the Naïve Bayes algorithm outperforms it with an accuracy of 92.23%. This suggests that the Naïve Bayes algorithm is more effective for this application. Nevertheless, both methods still have limitations that can be improved through parameter optimization and more in-depth data processing. The implementation of this data-driven system is expected to enhance the efficiency of providing more relevant major recommendations at SMK Negeri 4 Bandar Lampung and serve as an inspiration for other schools to adopt similar approaches to improve education quality.
Co-Authors - Nurjoko Abdi Darmawan Abdul Lathif Sampurna, Abdul Lathif Agung Purwanto Alexander, Ganesis Andani, Bethania Andani, Mia Andrean Danofic Anwar, Mashuril Besti Lilyana, Besti Destanto, Tri Effendi, Mukhammad Khoirul Febri Suganda Goesderilidar Hary Sabita Idris Asmuni Iskandar, Muhammad Yashlan ismail, Rendy Kristyanto, Andi Kurniawan, Putra M Said Hasibuan Muhamad Muslihudin Muhammad Nur Ikhsanto, Muhammad Nur Mukhlisin Mukhlisin Omega, Jessica Anatasya Riko Herwanto Rini Nurlistiani Sadimin Sadimin Sefriyanto, Eka Siti Mukodimah Sriyanto Sriyanto Susanti, Yus Sutedi Sutedi Triloka, Joko Wasilah Wasilah Windari, Ratih Y. Suhendro Yan Aditiya Pratama Yulian, Hengky Yuniwati Yuniwati, Yuniwati Yusuf, Suhendro Zatika, Dinda Anna