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All Journal Informatik : Jurnal Ilmu Komputer Jurnal ELTEK Inovate : Jurnal Ilmiah Inovasi Teknologi Informasi
Faizah, Arbiati
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Implementasi Algoritma Apriori Untuk Sistem Penilaian Kepuasan Mahasiswa Terhadap Pelayanan Di Universitas Hasyim Asy’ari (Studi Kasus di Universitas Hasyim Asy’ari) Ferdiansa, Ifan; Imam Agung, Achmad; Faizah, Arbiati
Inovate Vol 6 No 2 (2022): Maret
Publisher : Fakultas Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33752/inovate.v6i2.3169

Abstract

Student satisfaction assessment system is a system that be can used to determine the nature of student related services available the institution based on criteria in service quality. Academic services describe aspects that are superior and are invisible but can be felt by students. Quality academic services will create quality students. Asy'ari Hasyim University, one of the pesantren-based educational institutions is demanded to provide quality academic services. This research aims to improve services at Hasyim Asy'ari University, thereby increasing student satisfaction. In this study applying the Apriori Algorithm in conducting frequent itemset searches with the association rule technique. The writer uses a quantitative approach. Quantitative approach is a method that emphasizes the analysis of numerical data (numbers) obtained by the method itself. With the grouping of data like this, it is expected that the Hasyim Asy'ari University can assess and improve academic services. As well as knowing the relationship between a priori algorithm and student satisfaction with campus services, the level of student satisfaction is known as well as looking for inadequate services Keywords: Algoritmh Apriori, student satisfaction, service quality, quantitative
Sistem Penentuan Status Gizi Balita Menggunakan Metode Naïve Bayes Classifier (Studi Kasus Posyandu Anggrek Putih Seblak Desa Kwaron Chasanah, Nidhaul; Dwi Indriyanti, Aries; Faizah, Arbiati
Inovate Vol 6 No 2 (2022): Maret
Publisher : Fakultas Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33752/inovate.v6i2.3174

Abstract

Determination of toddler growth and development is very important to do to see if there is a growth disorder of toddlers from an early age by measuring body weight as the best way to assess the nutritional status of toddlers each month so that children's growth and development will be monitored by measuring toddlers and toddlers regularly, body weight and height. Posyandu is useful for providing services to the community about the importance of toddler development and nutritional status quickly and accurately. Therefore, in this research, make a system design as information about the nutritional status of toddlers us.ing th.e Naïve Bayes Classifier metod. This method is fairly sample classification metod by assuming the attribute classification. The calculation process using the Naive Bayes Classifier metod to determine the nutritional status of toddlers will go through 6 stages. So each new data will perform a probability with each existing class, the final result is seen from the highest value of this calculation which is used to see the results of determiining the nut.risio.nal th.e te.sted child.ren. Deter.mi.ning th.e nutritional status o. .f. toddlers by inputting age, sex, weight, height with three data on the nutrition categories of children under five, namely thin, normal, fat. System testing was carried out with 83 data on toddlers at Posyandu Anggrek Putih Dsn Seblak, Kwaron Village, each of which 53 toddler data as training data and 30 other toddler data were used for data testing with an accuracy value of 86.66%. Keywords: Determination of Toddler Nutritional Status, Classification, Naive Bayes Classifier
Komparasi Algoritma Support Vector Machine (SVM) dan Convolutional Neural Network (CNN) untuk Klasifikasi Ekspresi Wajah Faizah, Arbiati; Imron, Syaiful; Rewur, Afny; Makasunggal, Juan Natanel; Hari Saputro, pujo
Informatik : Jurnal Ilmu Komputer Vol 21 No 1 (2025): April 2025
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v21i1.11091

Abstract

Ekspresi wajah merupakan komponen penting dalam komunikasi nonverbal, karena mampu menyampaikan emosi tanpa perlu berkata-kata. Berbagai studi menyebutkan bahwa lebih dari 55% informasi emosional dalam komunikasi manusia disampaikan melalui ekspresi wajah. Dalam bidang pengolahan citra digital, klasifikasi ekspresi wajah menjadi salah satu tantangan yang banyak dikaji. Penelitian ini bertujuan untuk membandingkan performa algoritma Support Vector Machine (SVM) dan Convolutional Neural Network (CNN) dalam mengklasifikasikan empat ekspresi wajah: happy, sad, neutral, suprise. Data yang digunakan berasal dari dataset FER-2013 dengan 4000 gambar per kelas. Setiap citra melalui tahap preprocessing berupa konversi grayscale, normalisasi piksel, dan augmentasi data. Model Support Vector Machine (SVM) menghasilkan akurasi pelatihan sebesar 99,70%, namun akurasi validasinya hanya 41,47%, menandakan terjadinya overfitting. Sebaliknya, Convolutional Neural Network (CNN) memberikan hasil yang lebih stabil dengan akurasi pelatihan sebesar 85,08% dan akurasi validasi tertinggi mencapai 55,03%. Convolutional Neural Network (CNN) juga menunjukkan performa tertinggi dalam mengenali ekspresi suprise dengan akurasi 69%. Hasil penelitian ini menunjukkan bahwa Convolutional Neural Network (CNN) lebih unggul dalam mengenali pola visual kompleks dibandingkan Support Vector Machine (SVM). Dengan demikian, penelitian ini memberikan kontribusi dalam pemilihan metode klasifikasi citra wajah yang optimal dan relevan untuk implementasi sistem pengenalan ekspresi secara otomatis.
Co-Authors Achmad Imam Agung ANITA ANDRIANI, ANITA ARIES DWI INDRIYANTI, ARIES Augusta Jannatul Firdaus, Reza Baharudin, Ilham BAMBANG SUJATMIKO Bastian Nursyahputra, Andhika Chasanah, Nidhaul Ferdiansa, Ifan Hari Saputro, Pujo Ilham Zulhardani, Muhammad Imron, Syaiful Indana Lazulfa, Indana istiqomah istiqomah Makasunggal, Juan Natanel Mashuri, Chamdan Mujianto, Ahmad Heru Rewur, Afny Rofi’ul Azis, Moh. Zuchrotunisa