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All Journal JURNAL ILMIAH INFORMATIKA Informatik : Jurnal Ilmu Komputer IJIIS: International Journal of Informatics and Information Systems SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal ELTEK Journal of Innovative and Creativity Jurnal ELTEK Inovate : Jurnal Ilmiah Inovasi Teknologi Informasi
Arbiati Faizah
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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RANCANG BANGUN SISTEM PENENTUAN STATUS GIZI BALITA DENGAN METODE K-MEANS Sucipto , Hadi; Faizah , Arbiati
Inovate Vol 4 No 1 (2019): September
Publisher : Fakultas Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33752/inovate.v4i1.769

Abstract

Malnutrition or also called malnutrition is still a problem in Indonesia, especially in the toddler agegroup. Lack of nutritional intake at an early age will have a negative impact on the growth anddevelopment of infants, which will further hamper some of the developmental processes of the toddler,such as learning to talk, walk, eat, and other activities. In this study the design of the nutritional statusdetermination system for toddlers is discussed using the K-Means method. The data will be groupedinto six clusters, namely baduta or under-fives, under-five or under-five, under-five or under-line(BGM), under-five or under-five, under-five or fat under-five, and under-five or under-five. By usingthe K-Means method, the data of children under five or under two years will be grouped into clustersthat have been determined. The results of this study are the design of a system for determining thenutritional status of children under five in the form of a clustering system for the nutritional status ofchildren with an effective level of valid data from the implementation of a design system fordetermining the status of a toddler with the K-Means Method is 77.3 percent.Keywords: Cluster, K-Means, Nutrition, Toddler .
PEMANFAATAN MICROCONTROLLER ARDUINO UNO UNTUK SISTEM MONITORING SUHU DAN KELEMBABAN KUMBUNG JAMUR TIRAM Faizah, Arbiati; Hari Saputro, Pujo; Augusta Jannatul Firdaus, Reza
Inovate Vol 4 No 1 (2019): September
Publisher : Fakultas Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33752/inovate.v4i1.770

Abstract

Mushrooms are a high source of nutrition and can be processed into various types of cuisine. Oystermushroom cultivation in low-lying areas requires controlling temperature and humidity in mushroomkumbung to get optimal growth. Currently the system of regulating the temperature and humidity ofmushroom kumbung is done manually and automatically. This study aims to design and create anautomatic temperature and humidity monitoring system for oyster mushroom kumbung by utilizing anArduino Uno microcontroller and a DHT11 temperature sensor. The system design is made throughtwo phases namely hardware design and software design. The hardware design using the Arduino Unomicrocontroller and DHT11 sensor aims to monitor the temperature and humidity of the kumbung andsend data to the server. Whereas hardware design is implemented in an android application as aninterface in receiving data from the server and delivering the results of temperature and humiditymonitoring to farmers. The system that was made has been implemented in the oyster mushroomkumbung and tested temperature and humidity successfully and can send data to the server.Keywords: Arduino Uno, Microcontroller, Sensor DHT 11, Monitoring System.
Sistem Prediksi Persediaan Barang Menggunakan Metode Regresi Linier Berbasis Website Bastian Nursyahputra, Andhika; Dwi Indriyanti, Aries; Faizah, Arbiati
Inovate Vol 5 No 1 (2020): September
Publisher : Fakultas Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33752/inovate.v5i1.3072

Abstract

The prediction process is important for the company in the formulation of the company's strategy in the future. Therefore, a precise prediction method is needed by the company to be able to maximize the estimation of future sales. The Double Exponential Smoothing method is a popular method used in privacy because it has good performance. This method has parameter values and has a large influence on the results of predictions. This method uses data compilation that shows trends. Exponential smoothing in the presence of a trend such as a simple transmitter such as two components must be updated every period - its level and trend. Level is an estimate that is smoothed from the data value at the end of each period. A trend is a smoothed estimate of average growth. The purpose of this design produces a prediction method that is appropriate and applicable in the company to facilitate sales activities in the company. With the right prediction method, it is expected that the company can make efficient all the resources needed by the company. Keywords: Exponential Smoothing, Multiple Exponential Smoothing, level.
Sistem Informasi Manajemen Persediaan Bahan Baku Dengan Menggunakan Metode Economic Order Quantity (EOQ) Dan Just In Time (JIT) Berbasis Web Rofi’ul Azis, Moh.; Mashuri, Chamdan; Faizah, Arbiati
Inovate Vol 5 No 1 (2020): September
Publisher : Fakultas Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33752/inovate.v5i1.3075

Abstract

Raw material inventory management is very important to be carried out by companies in the process of production activities. The importance of inventory management is also used to address the causes of stock outages and waste of company funding sources. So that further analysis is required by using methods related to inventory management. In addition to further analysis, a computerized system is also needed that can manage raw material inventory management so that it can process automatically and is judged to be more effective. The method used in the study uses the Economic Order Quantity (EOQ) and Just-in-Time (JIT) methods. Both methods were chosen because they are more suitable to be applied in a company problem, especially in the matter of raw material inventory management. Of the two methods will be carried out a comparison process to find out which method is more effective when used. The results of this study are computerized systems used to determine raw material inventories by the EOQ and JIT methods. From the results of the calculation of the two methods, the final result of the JIT method is Rp. 3.654.495, while the EOQ method obtained a value of Rp. 6.603.181. By looking at the values generated by the two methods, it can be concluded that the JIT method is more efficient than the EOQ method. Keywords: EOQ, JIT, Raw material inventory
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.
Analysis of Information Technology Investment Management Strategy in Organizations: Case Studies with a Qualitative Approach Muhammad Amanulloh Mz; Arbiati Faizah; Nur Ismianah
Journal of Innovative and Creativity Vol. 5 No. 2 (2025)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joecy.v5i2.1458

Abstract

Information technology (IT) investment plays a strategic role in improving organizational performance in the digital era. This research aims to analyze effective IT investment management strategies in supporting the achievement of organizational goals. Using a qualitative approach through case studies in the public and private sectors, the study found that integration between business and IT strategies drives operational efficiency and innovation. The main challenges faced include rapid technological change, budget constraints, and lack of human resource capabilities. However, great opportunities arise from the use of artificial intelligence, cloud computing, and national digital transformation support. This study recommends the implementation of value-based IT investment management to ensure that each investment makes a tangible contribution to improving the organization's performance in a sustainable manner.
PREDIKSI KELULUSAN MAHASISWA MENGGUNAKAN ALGORITMA XGBOOST Imron, Syaiful; Faizah, Arbiati; Sugianto, Sugianto
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 9 No 1 (2026): Jurnal SKANIKA Januari 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v9i1.3647

Abstract

Student graduation times are often difficult to predict early, a major challenge facing institutions. Manual evaluations often fail to identify problematic students, leading to inaccurate graduation times that are detrimental to both students and institutions. This is crucial because study duration and timely graduation are important criteria in assessing institutional accreditation and quality. As an innovative solution, this study developed a graduation prediction model using the XGBoost and Random Forest algorithm, applying hyperparameter optimization techniques through Grid Search Cross Validation. The results showed that with default parameters, Random forest was superior to XGBoost. However, after hyperparameter tuning, XGBoost achieved better accuracy than Random Forest with a significant increase in accuracy, from 88.15% to 92.66% (precision 91.87%, recall 91.67%, and F1-score 91.38%). This confirms that appropriate hyperparameter tuning is a strategic key to maximizing the effectiveness of classification models. Thus, this model can be a tool for institutions to monitor and intervene early on in potential student delays.
Co-Authors Abda Abda Achmad Imam Agung Anita Andriani ARIES DWI INDRIYANTI, ARIES Augusta Jannatul Firdaus 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 istiqomah istiqomah Istiqomah Istiqomah Makasunggal, Juan Natanel Mashuri, Chamdan Muhammad Amanulloh Mz Mujianto, Ahmad Heru Nur Ismianah Nur Rachman Dzakiyullah Pujo Hari Saputro Rewur, Afny Reza Augusta J.F Rofi’ul Azis, Moh. Sucipto , Hadi Sugianto Sugianto Zuchrotunisa