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All Journal ARABIYAT Scientific Journal of Informatics Sistemasi: Jurnal Sistem Informasi Jurnal CoreIT Seminar Nasional Teknologi Informasi Komunikasi dan Industri INOVTEK Polbeng - Seri Informatika Jurnal Informatika Universitas Pamulang Building of Informatics, Technology and Science Zonasi: Jurnal Sistem Informasi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Computer Science and Information Technology (CoSciTech) Knowbase : International Journal of Knowledge in Database Jurnal Komtika (Komputasi dan Informatika)
Nazir, Alwis
UIN Sultan Syarif Kasim Riau
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics

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Analisa Perbandingan Metode Trend Moment dan Regresi Linear dalam Prediksi Kurs Mata Uang Rupiah terhadap Mata Uang Riyal Ananda, Rahmadan Alam Ardan; Nazir, Alwis; Oktavia, Lola; Haerani, Elin; Insani, Fitri
Journal of Computer System and Informatics (JoSYC) Vol 6 No 3 (2025): May 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i3.7400

Abstract

Currency exchange rates play an important role in the economic stability of a country, especially in the context of international trade and global financial mobility. In Indonesia, fluctuations in the Rupiah exchange rate against the Saudi Arabian Riyal (SAR) have become a strategic issue, especially ahead of the Hajj season. This study aims to predict the exchange rate of Rupiah against Riyal in that period by using two forecasting approaches, namely Linear Regression and Trend Moment. The performance evaluation of both methods is conducted based on historical data using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) indicators. The results show that Linear Regression provides a better level of accuracy with an MAE of 330.36 and a MAPE of 17.32%, compared to Trend Moment which has an MAE of 412.41 and a MAPE of 18.88%. This finding shows that Linear Regression is more effective in capturing the pattern of exchange rate changes that tend to be linear. The prediction results also show an increasing trend in the exchange rate ahead of the Hajj month, which correlates with the increasing demand for foreign exchange. The implications of these results can be utilized by prospective pilgrims, business actors, and the government in formulating more appropriate and adaptive financial strategies
KLASIFIKASI STATUS STUNTING BALITA MENGGUNAKAN METODE C4.5 BERBASIS WEB Fauzan Adzim; Budianita, Elvia; Nazir, Alwis; Syafria, Fadhilah
ZONAsi: Jurnal Sistem Informasi Vol. 5 No. 3 (2023): Publikasi artikel ZONAsi: Jurnal Sistem Informasi Periode September 2023
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/zn.v5i3.15828

Abstract

Stunting pada balita merupakan permasalahan serius yang perlu diselesaikan karena berdampak negatif pada pertumbuhan dan perkembangan anak. Stunting adalah keadaan dimana balita mengalami kekurangan gizi yang kronis sehingga pertumbuhan fisik dan tinggi badannya tidak sejalan dengan usianya. Pola makan yang tidak memadai dan nutrisi yang tidak sesuai menjadi sebab terjadinya stunting pada balita. Dalam upaya pencegahan stunting dilakukan pemantauan terhadap status gizi dan tumbuh kembang balita setiap bulan di posyandu terdekat. Untuk menentukan status balita normal atau stunting masih menggunakan cara manual berdasarkan metode antropometri sehingga dapat meningkatkan risiko kesalahan dalam perhitungan atau penginputan data. Menggunakan teknik Data mining dapat menentukan klasifikasi atau prediksi pada status stunting balita dengan menganalisis pola data yang telah ada sebelumnya. C4.5 adalah algoritma klasifikasi terkenal dan familiar dan sering digunakan dengan menggunakan teknik pohon keputusan juga mempunyai keunggulan seperti mampu mengolah data numerik (kontinu) dan diskrit, merapikan nilai atribut yang tidak lengkap, menciptakan aturan yang mudah dimengerti, serta kecepatan pemprosesan yang relatif cepat dibandingkan dengan algoritma lainnya adapun dataset yang digunakan terdiri dari atribut umur, jenis kelamin, indeks menyusui dini (IMD), berat badan, dan tinggi badan. Evaluasi model dilakukan dengan mempergunakan confusion matrix dan menghasilkan tingkat akurasi terbaik sebesar 93.62%. Hasil ini diperoleh dari pemisahan data sebanyak 80% data latih sebanyak 20% data uji dengan dengan Max Depth sebesar 10 dan jumlah seluruh data sebanyak 1172.
Klasifikasi Status Stunting Balita Menggunakan Metode Naïve Bayes Gaussian Berbasis Web Mulyono, Makmur; Budianita, Elvia; Nazir, Alwis; Syafria, Fadhilah
Jurnal Informatika Universitas Pamulang Vol 8 No 3 (2023): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v8i3.33399

Abstract

The growth and development of toddlers must get attention from parents because toddlerhood is a golden period in shaping the growth and development and intelligence of children. Stunting is  a state of malnutrition in which stunted growth and development of children and this is included in chronic nutritional problems, the incidence of stunting  can be seen from height that is not in accordance with age. In preventing toddlers from stunting, it is necessary to anticipate early prevention by conducting examinations at the nearest posyandu which is measured using anthropometric methods. The calculation  of stunting or normal status based on anthropometric data is generally processed manually so that there is a high possibility of errors in calculating and entering data. Data mining can make classifications or predictions on the stunting status  of toddlers by studying previous data patterns. Naïve bayes is one classification method that has the advantage of high accuracy with little training data as for the attributes used in this study, namely age, gender, Early Initiation of Breastfeeding (IMD), weight, height. Based on the test results, the best average accuracy was obtained on numerical data types for age, weight, height and nominal gender attributes, Early Breastfeeding Initiation (IMD) with the highest accuracy in the 80:20 data comparison, which is 80.34% with a total of 1172 data.
Implementasi Data Mining Association Rules Menggunakan Algoritma Fp-Growth untuk Data Penjualan Keramik Isra Almahsa, Muhammad; Nazir, Alwis; Afriyanti, Iis; Budianita, Elvia
Jurnal Informatika Universitas Pamulang Vol 8 No 3 (2023): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v8i3.34442

Abstract

The ceramic company CV Sukses Bersama is facing challenges in determining the optimal product layout and promotion strategy. To address this issue, this research applies the Data Mining Association Rules method using the FP-Growth algorithm. With the Python programming language, the author conducts an analysis of the company's sales data to identify significant purchasing patterns. The analysis results reveal that the product 'MCC' enjoys an exceptionally high level of popularity, with a support rate reaching 94.86%. This indicates that 'MCC' is the primary favorite among CV Sukses Bersama's customers. The analysis also unveils several significant Association Rules, such as {'MCC'} -> {'HRM'} with a confidence level of 86.99%. This implies that customers who purchase 'MCC' tend to buy 'HRM' with a high level of certainty. These findings hold strategic importance for CV Sukses Bersama, offering valuable insights that can be utilized to design more effective marketing strategies by understanding customer preferences and optimizing product stock management.
Implementasi Data Mining Untuk Prediksi Stok Penjualan Keramik dengan Metode K-Means Dinata, Ferdian Arya; Nazir, Alwis; Fikry, Muhammad; Afrianty, Iis
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5200

Abstract

Ceramics has become one goods that consumers show interest in every year, so many companies are interested in selling ceramics. However, ceramic sales must meet and balance changing customer needs as well as problems found regarding ceramic products and customers, such as a lack of stock of ceramic products which results in customers not placing orders and product sales not meeting targets. So it is necessary to group ceramics to anticipate the risks that the company will accept by utilizing the data mining process using past data. This research uses the K-Means method found in data mining. The objective of this research is to group determine sales of brands that have potential for additional stock in the future and to test the data using the DBI (Davies Bouldin Index) which is carried out by testing the distance values between clusters through a series of experiments. This research uses data for the last 1 year from January 2022 to December 2022 with a total of 156 data using 9 attributes, namely brand, item code (FT, WT) and size (40x40, 25x25, 50x50, 25x40, 60x60, 20x40). The results of the research using the K-Means method, the best-selling brand is cluster 2, the best-selling brand is cluster 1 and the best-selling brand is cluster 0. The best-selling brand is HRM, the best-selling brand is VALENSIA and the best-selling brand is MCC. Test results using the DBI method with a validity of 01.013 show that the best cluster is obtained at k=3 using the elbow method. It is hoped that this research will contribute to related companies as support for decision making.
PEMBELAJARAN BAHASA ARAB BERBASIS TEKNOLOGI INFORMASI DAN KOMUNIKASI DI KOTA PADANG Ritonga, Mahyudin; Nazir, Alwis; Wahyuni, Sri
Arabiyat : Jurnal Pendidikan Bahasa Arab dan Kebahasaaraban Vol. 3 No. 1 (2016)
Publisher : Syarif Hidayatullah State Islamic University of Jakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/a.v3i1.2879

Abstract

The integration of various fields of sciences and technology is vital in this digital era, including for educational purposes such as the utilization of Information and Communication Technology (ICT) in learning Arabic. However, an obstacle faced by educational institutions is they do not have a clear model in the use of ICT to apply in teaching learning process; therefore, it is an urgent need to conduct a study to find out the best model of ICT-based Arabic learning. This study used qualitative research method covering three stages, i.e. the preliminary, development and implementation phases. The research used purposive sampling and data collection techniques were observation, interviews and documentation. Data analysis technique was the analytical techniques developed by Miles and Hubermas. The result showed that the design of ICT-based Arabic learning model that developed at MTsN Kota Padang is "al-Hasshub al-ittishali model". It is a computerized-based Arabic communicative teaching model. By implementing this model, the material and other learning media are designed using a computer program. Moreover, the teacher served as a learning motivator and mediator on materials that need more explanation.DOI : 10.15408/a.v3i1.2879
Penerapan K-Means Clustering Pada Data Obat/Alkes di Apotik RSUD Selasih Budianita, Elvia; Haerani, Elin; Nazir, Alwis
Seminar Nasional Teknologi Informasi Komunikasi dan Industri 2023: SNTIKI 15
Publisher : UIN Sultan Syarif Kasim Riau

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

Abstract

Apotik merupakan salah satu tempat yang menjual obat-obatan, alat kesehatan (alkes) dan lainnya. Salah satu faktor penting untuk kelangsungan proses jual beli pada apotik yaitu adanya persediaan obat-obatan. Apotik RSUD Selasih sudah memiliki sistem yang menampung data persediaan obat obatan. Sistem tersebut juga memiliki data transaksi penjualan obat/alkes dan data pasien. Namun, persediaan obat-obatan dilakukan hanya dengan memeriksa persediaan obat yang hampir habis kemudian memperbarui stok persediaan obat tersebut sehingga hal ini kurang efisien jika suatu waktu membutuhkan obat dalam jumlah yang besar dan ternyata stok habis. Pada penelitian ini diterapkan suatu metode data mining K-Means Clustering dengan cara menganalisa pada pemakaian obat untuk menghasilkan informasi yang dapat dijadikan sebagai perencanaan dan pengendalian persediaan obat berdasarkan hasil kluster yang terbentuk. Berdasarkan hasil pengujian yang telah dilakukan menggunakan Davies Bouldin Index, diperoleh jumlah kluster terbaik adalah 2 dengan nilai DBI sebesar 0,33 yaitu kluster yang memiliki permintaan yang tinggi dengan penjualan obat selama 12 bulan diatas 3200 buah dan kluster yang memiliki permintaan yang rendah dengan penjualan obat/alkes selama 12 bulan dibawah 3200 buah.
Comparison of Triple Exponential Smoothing and Support Vector Regression Algorithms in Predicting Drug Usage at Puskesmas Agnesti, Syafira; Nazir, Alwis; Iskandar, Iwan; Budianita, Elvia; Afrianty, Iis
Sistemasi: Jurnal Sistem Informasi Vol 12, No 3 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i3.3499

Abstract

Drug management is important in managing adequate drug supplies in Puskesmas, to avoid errors in controlling existing drug stock inventory, it is necessary to predict the amount of drug usage by comparing Data Mining methods and Machine Learning methods, using the Triple Exponential Smoothing (TES) and Support Vector Regression (SVR) algorithms. Implementation is done using the Python programming language. The data used is Amlodipine 10 mg and Amoxicillin 500 mg drug data with a period of 42 months, from January 2020 - June 2023. This study aims to determine the best algorithm by comparing prediction error rate using the Mean Absolute Percentage Error (MAPE) method. Based on research that has been conducted on Amlodipine 10 mg and Amoxicillin 500 mg drugs with a division of 80% training data and 20% testing data, the Triple Exponential Smoothing algorithm with an additive model produces MAPE values of 10.36% and 17.50% respectively with the "Good" category. While Support Vector Regression algorithm, with RBF kernel, complexity 1.0, and epsilon 0.1 produces MAPE values of 10.31% and 9.38% in the "Good" and "Very Good" categories, respectively. Based on this, it can be concluded that Support Vector Regression algorithm is better at predicting than the Triple Exponential Smoothing algorithm.
Penerapan Algoritma Apriori pada Transaksi Penjualan Produk Cat untuk Meningkatkan Strategi Bisnis Mulyati, Sabar; Nazir, Alwis; Budianita, Elvia; Cynthia, Eka Pandu
Journal of Information System Research (JOSH) Vol 5 No 2 (2024): Januari 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i2.4725

Abstract

Data mining is a combination of data analysis techniques and determining important patterns in the data. Data mining can also be used to improve business progress. In this research, data mining is used to improve sales business strategies at CV. Sumber Tirta Anugerah in the last 1 year. Previously CV. Sumber Tirta Anugerah does not apply the a priori method to sales, causing product stock to pile up. Data mining is assisted by an a priori algorithm to determine the frequency of itemsets in looking for patterns of items that are usually purchased by customers at the same time. In this research, several items were used such as Lippo coupons, Lippo Emultion, Lektone Emultion, Lippo waterproof, Japanese Duco paint, Kansai Tropical, Beta Chemie, Flalit, and cable clamps, synthetic property, Tajima New putty and Bioton Emultion. Based on the research that has been carried out, the largest support value is 32.18% for 1 itemset. Then for the 2 itemsets the largest support was found at 9.32%. Next, the 3 itemsets obtained the largest support of 1.94%. So based on the overall data, the confidence is 72.97% and the lift ratio test value is 2.22%.
Application of Data Mining for Ceramic Sales Data Association Using Apriori Algorithm Habibi, M. Ilham; Nazir, Alwis; Haerani, Elin; Budianita, Elvia
Knowbase : International Journal of Knowledge in Database Vol. 4 No. 2 (2024): December 2024
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/knowbase.v5i2.8757

Abstract

This research is conducted to provide an understanding of consumer purchasing patterns at CV. Sukses Bersama by applying data mining using the association rules method and the Apriori algorithm to identify the relationships between one item that influences other items within a ceramic sales dataset at CV. Sukses Bersama. This information is expected to serve as a foundation for improving sales strategies, optimizing customer satisfaction, and expanding the company's market share. The Apriori algorithm is a popular algorithm implemented to identify association rules in data mining. The Apriori algorithm was chosen due to its ability to efficiently identify association rules and its good scalability in handling large datasets. This research begins with the collection of ceramic sales data, followed by data preprocessing to clean and prepare the data. The Apriori algorithm is then applied to discover the association rules, which generate two matrices: support and confidence, and the results are subsequently evaluated. This research was conducted using Google Colaboratory, a web application that is a cloud-based platform provided by Google to run Python code. The results of the study show that the Apriori algorithm can depict significant association structures between different ceramic brand types in the sales data of CV. Sukses Bersama. The calculation results show that the rule has the maximum support and confidence value, namely 67% support value and 84% confidence value in the rule "if you buy the DIAMD brand, you will buy the TOTAL brand"
Co-Authors .Safrizal, Safrizal Abdul Wahid Adzhima, Fauzan Afriyanti, Iis Afriyanti, Liza Agnesti, Syafira Akbar, Lionita Asa Akhyar, Amany Ananda, Rahmadan Alam Ardan Berliana, Trisia Intan Dinata, Ferdian Arya Eka Pandu Cynthia, Eka Pandu Elin Haerani Elvia Budianita Fadhilah Syafria Fauzan Adzim Fitri Insani Fitria Novitasari Gusti, Siska Kurnia Habibi, M. Ilham Hamwar, Syahbudin Hasibuan, Aldiansyah Pramudia Iis Afrianty Isra Almahsa, Muhammad Jasril Jasril Mahyudin Ritonga Muhammad Abdillah Muhammad Fikry Muhammad Irsyad Mulyati, Sabar Mulyono, Makmur Najmi, Risna Lailatun Nurfadilah, Nova Siska Oktavia, Lola Pizaini Pizaini Putri. P, Zurneli Kurnia Rahmadhani, R. Reni Susanti Sri Wahyuni Surya Agustian Suwanto Sanjaya Syafria, Fadhillah Syaputra, Muhammad Dwiky Wiranti, Lusi Diah Yusra Yusra, - Yusra, Yusra