cover
Contact Name
Siti Mutrofin
Contact Email
sitimutrofin@ft.unipdu.ac.id
Phone
+6287852416880
Journal Mail Official
teknologi@ft.unipdu.ac.id
Editorial Address
Fakultas Sains dan Teknologi, Prodi Sistem Informasi, Unipdu Kompleks Ponpes Darul 'Ulum Peterongan, Jombang, Jawa timur, 61481
Location
Kab. jombang,
Jawa timur
INDONESIA
TEKNOLOGI: Jurnal Ilmiah Sistem Informasi
ISSN : 20878893     EISSN : 25273671     DOI : -
TEKNOLOGI: Jurnal Ilmiah Sistem Informasi published by the Department of Information Systems Unipdu Jombang. TEKNOLOGI published twice a year, in January and July, TEKNOLOGI includes research in the field of Information Technology Design and Development of Information Systems; Business intelligence; Functions and Organization Management Information Systems; and others. Editors invite lecturers researchers, reviewers, practitioners, industry, and observers to contribute to this journal. The language used in the form of Indonesian and English. TEKNOLOGI is the national scientific journals are open to seeking innovation, creativity and novelty. Either in the form of letters, research notes, Articles, supplemental Articles Articles or reviews in the field of information systems and information technology. TEKNOLOGI aims to achieve state-of-the-art in the theory and application of this field. TEKNOLOGI provide a platform for scientists and academics across Indonesia to promote, share, and discuss new issues and the development of information systems and information technology.
Articles 10 Documents
Search results for , issue "Vol 11, No 2 (2021): July" : 10 Documents clear
Penerapan metode k-means clustering data COVID-19 di Provinsi Jakarta Untoro, Meida Cahyo; Anggraini, Leslie; Andini, Maria; Retnosari, Hesti; Nasrulloh, M. Anas
TEKNOLOGI: Jurnal Ilmiah Sistem Informasi Vol 11, No 2 (2021): July
Publisher : Universitas Pesantren Tinggi Darul 'Ulum (Unipdu) Jombang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/teknologi.v11i2.2323

Abstract

The disease epidemic that attacked the respiratory area and was detected in Indonesia starting in early 2020 is the Corona Virus (COVID-19). This virus's spread is relatively easy, namely through droplets from infected patients, so that the spread is very rapid. This research was conducted to cluster the data on Covid-19 cases in Jakarta Province considering that Jakarta is the starting point for the first case of Corona in Indonesia and until now has become one of the most significant contributors to COVID-19 issues in Indonesia, namely as of December 2020 positive cases of Covid-19 reached 154,000. Souls with the healing of 139.0000 souls. The grouping was carried out based on positive and dead patients from each urban village in Jakarta Province. This study uses the k-means Method to cluster in the handling of COVID-19 cases with 2 clusters. Data distribution in cluster 1 consists of 173 data and 18 data in cluster 2. The use of k-means in this study provides information on areas with the highest and lowest number of positive cases and the highest and lowest cure rates that can be used as an evaluation in handling the Covid-virus 19.
Penerapan metode k-means clustering data COVID-19 di Provinsi Jakarta Untoro, Meida Cahyo; Anggraini, Leslie; Andini, Maria; Retnosari, Hesti; Nasrulloh, M. Anas
TEKNOLOGI: Jurnal Ilmiah Sistem Informasi Vol 11, No 2 (2021): July
Publisher : Universitas Pesantren Tinggi Darul 'Ulum (Unipdu) Jombang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/teknologi.v11i2.2323

Abstract

The disease epidemic that attacked the respiratory area and was detected in Indonesia starting in early 2020 is the Corona Virus (COVID-19). This virus's spread is relatively easy, namely through droplets from infected patients, so that the spread is very rapid. This research was conducted to cluster the data on Covid-19 cases in Jakarta Province considering that Jakarta is the starting point for the first case of Corona in Indonesia and until now has become one of the most significant contributors to COVID-19 issues in Indonesia, namely as of December 2020 positive cases of Covid-19 reached 154,000. Souls with the healing of 139.0000 souls. The grouping was carried out based on positive and dead patients from each urban village in Jakarta Province. This study uses the k-means Method to cluster in the handling of COVID-19 cases with 2 clusters. Data distribution in cluster 1 consists of 173 data and 18 data in cluster 2. The use of k-means in this study provides information on areas with the highest and lowest number of positive cases and the highest and lowest cure rates that can be used as an evaluation in handling the Covid-virus 19.
Perbandingan metode Double Exponential Smoothing dan Simple Moving Average pada kasus peramalan penjualan Hariri, Fajar Rohman; Sari, Waskita; Mashuri, Chamdan
TEKNOLOGI: Jurnal Ilmiah Sistem Informasi Vol 11, No 2 (2021): July
Publisher : Universitas Pesantren Tinggi Darul 'Ulum (Unipdu) Jombang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/teknologi.v11i2.2348

Abstract

Toko Bangunan Barokah (TB. Barokah) yang berada di Singosari, Malang, Jawa Timur adalah salah satu toko yang menjual bahan bangunan. Beberapa barang yang dijual adalah semen putih dan semen hitam. Jumlah penjualan semen setiap bulan sangat beragam dan fluktuatif. Semen hitam menempati angka sangat tinggi dibandingkan semen hitam pada grafik penjualan setiap bulannya. Begitu juga dengan semen hitam merek Bosowa, semen putih merek Gresik dan merek Tiga Roda meskipun permintaan kecil, tetapi masih terjadi pergerakan jumlah penjualannya. Jumlah permintaan yang sangat fluktuatif menjadikan jumlah persediaan produk yang disiapkan tidak pasti, yang dipengaruhi oleh jumlah jenis dan merek, sehingga manajemen persediaan produk kesulitan dalam penyediaannya. Penelitian ini bertujuan untuk meramal jumlah semen yang terjual di bulan berikutnya, serta untuk mengetahui kinerja antara kedua metode Double Exponential Smoothing (DES) dan Simple Moving Average (SMA) untuk melakukan forecasting hasil penjualan semen pada TB. Barokah. Metode SMA mampu melakukan peramalan dengan data permintaan atau penjualan yang stabil/konstan. Sedangkan metode DES mampu dan dapat memberikan nilai pada bobot secara bertingkat dengan data up to date. DES mampu melakukan peramalan penjualan semen tiap bulan dengan nilai rata-rata percentange error (PE) 0,14%, sedangkan SMA dengan nilai rata-rata PE 1,35%.  Berdasarkan hasil pengujian menggunakan data dari TB. Barokah didapatkan bahwa metode yang paling efektif adalah DES karena memiliki nilai PE lebih kecil dibandingkan dengan SMA.
Perbandingan metode Double Exponential Smoothing dan Simple Moving Average pada kasus peramalan penjualan Hariri, Fajar Rohman; Sari, Waskita; Mashuri, Chamdan
TEKNOLOGI: Jurnal Ilmiah Sistem Informasi Vol 11, No 2 (2021): July
Publisher : Universitas Pesantren Tinggi Darul 'Ulum (Unipdu) Jombang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/teknologi.v11i2.2348

Abstract

Toko Bangunan Barokah (TB. Barokah) yang berada di Singosari, Malang, Jawa Timur adalah salah satu toko yang menjual bahan bangunan. Beberapa barang yang dijual adalah semen putih dan semen hitam. Jumlah penjualan semen setiap bulan sangat beragam dan fluktuatif. Semen hitam menempati angka sangat tinggi dibandingkan semen hitam pada grafik penjualan setiap bulannya. Begitu juga dengan semen hitam merek Bosowa, semen putih merek Gresik dan merek Tiga Roda meskipun permintaan kecil, tetapi masih terjadi pergerakan jumlah penjualannya. Jumlah permintaan yang sangat fluktuatif menjadikan jumlah persediaan produk yang disiapkan tidak pasti, yang dipengaruhi oleh jumlah jenis dan merek, sehingga manajemen persediaan produk kesulitan dalam penyediaannya. Penelitian ini bertujuan untuk meramal jumlah semen yang terjual di bulan berikutnya, serta untuk mengetahui kinerja antara kedua metode Double Exponential Smoothing (DES) dan Simple Moving Average (SMA) untuk melakukan forecasting hasil penjualan semen pada TB. Barokah. Metode SMA mampu melakukan peramalan dengan data permintaan atau penjualan yang stabil/konstan. Sedangkan metode DES mampu dan dapat memberikan nilai pada bobot secara bertingkat dengan data up to date. DES mampu melakukan peramalan penjualan semen tiap bulan dengan nilai rata-rata percentange error (PE) 0,14%, sedangkan SMA dengan nilai rata-rata PE 1,35%.  Berdasarkan hasil pengujian menggunakan data dari TB. Barokah didapatkan bahwa metode yang paling efektif adalah DES karena memiliki nilai PE lebih kecil dibandingkan dengan SMA.
Analisis perbandingan algoritma Naïve Bayes, k-Nearest Neighbor dan Neural Network untuk permasalahan class-imbalanced data pada kasus credit card fraud dataset Sahroni, Mita Yuanika; Setifani, Niken Ayu; Fitriana, Devinta Nurul
TEKNOLOGI: Jurnal Ilmiah Sistem Informasi Vol 11, No 2 (2021): July
Publisher : Universitas Pesantren Tinggi Darul 'Ulum (Unipdu) Jombang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/teknologi.v11i2.2393

Abstract

The high public interest in transactions using credit cards in the banking sector has the potential for higher credit card fraud. This study uses a credit card fraud dataset that consisting of 284,807 data obtained from Kaggle. The dataset in this study is class-imbalanced data with a comparison between the major class of 99.8% and the minor class of 0.2%. This class-imbalanced data problem will be solved by applying undersampling. In order to determine the performance of the classification algorithm that is most suitable for solving class-imbalanced data problems, a comparison of the Naïve Bayes, k-Nearest Neighbor (kNN) and Neural Network algorithms will be carried out. The t-test in this study was conducted to determine the significance of differences between algorithms. Algorithm performance evaluation uses accuracy and AUC (area under the curve) values. The test results in this study is Neural Network has better performance than other algorithms because it has the highest accuracy value of 93.59% and AUC value of 0.977. Based on the t-test results, the Neural Network with k-NN has a significant difference, in contrast to the Neural Network with Naïve Bayes there is no significant difference.
Analisis perbandingan algoritma Naïve Bayes, k-Nearest Neighbor dan Neural Network untuk permasalahan class-imbalanced data pada kasus credit card fraud dataset Sahroni, Mita Yuanika; Setifani, Niken Ayu; Fitriana, Devinta Nurul
TEKNOLOGI: Jurnal Ilmiah Sistem Informasi Vol 11, No 2 (2021): July
Publisher : Universitas Pesantren Tinggi Darul 'Ulum (Unipdu) Jombang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/teknologi.v11i2.2393

Abstract

The high public interest in transactions using credit cards in the banking sector has the potential for higher credit card fraud. This study uses a credit card fraud dataset that consisting of 284,807 data obtained from Kaggle. The dataset in this study is class-imbalanced data with a comparison between the major class of 99.8% and the minor class of 0.2%. This class-imbalanced data problem will be solved by applying undersampling. In order to determine the performance of the classification algorithm that is most suitable for solving class-imbalanced data problems, a comparison of the Naïve Bayes, k-Nearest Neighbor (kNN) and Neural Network algorithms will be carried out. The t-test in this study was conducted to determine the significance of differences between algorithms. Algorithm performance evaluation uses accuracy and AUC (area under the curve) values. The test results in this study is Neural Network has better performance than other algorithms because it has the highest accuracy value of 93.59% and AUC value of 0.977. Based on the t-test results, the Neural Network with k-NN has a significant difference, in contrast to the Neural Network with Naïve Bayes there is no significant difference.
Klasifikasi citra makanan/non makanan menggunakan metode Transfer Learning dengan model Residual Network Thiodorus, Gustavo; Prasetia, Anugrah; Ardhani, Luthfi Afrizal; Yudistira, Novanto
TEKNOLOGI: Jurnal Ilmiah Sistem Informasi Vol 11, No 2 (2021): July
Publisher : Universitas Pesantren Tinggi Darul 'Ulum (Unipdu) Jombang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/teknologi.v11i2.2402

Abstract

People's activities that often upload photos of food before eating are common in popular social media, such as Facebook, Instagram, Twitter, and Pinterest. The food image data circulating on social media can be used for business purposes, such as analyzing customer behavior patterns. However, not all of the uploaded images are food images, so that before the analysis is carried out, it is necessary to do a classification task between food images and non-food images. Therefore, the researcher proposes automatic food image classification using transfer learning method using the Residual Network model version of 18 (ResNet-18). Residual Network model is used because it has a residual connection mechanism to solve the vanishing gradient problem. In addition, transfer learning was chosen because this method leverages the features and weights that have been generated in the previous training process on large and more general data (Imagenet) and thus reduce computation time and increase accuracy. The test was carried out by comparing the capabilities of the ResNet18 model with AlexNet. In addition, the fine tuning and freeze layer methods used to improve the quality of the model were also carried out in this study. In the experiment, the data set was divided into 3,000 images for training data and 1,000 images for test data, while the evaluation used was correctness accuracy. The results obtained in the ResNet18 model, namely the fine tuning training method, produced an accuracy value of 0.981 while the freeze layer resulted in the best accuracy value of 0.988. The AlexNet model that uses the fine tuning training method produces an accuracy value of 0.970 while the freeze layer produces the best accuracy value of 0.978. It can be concluded that the mechanism with the best accuracy is found in the RestNet18 architecture using the freeze layer 1-3 with an accuracy of 0.988.
Klasifikasi citra makanan/non makanan menggunakan metode Transfer Learning dengan model Residual Network Thiodorus, Gustavo; Prasetia, Anugrah; Ardhani, Luthfi Afrizal; Yudistira, Novanto
TEKNOLOGI: Jurnal Ilmiah Sistem Informasi Vol 11, No 2 (2021): July
Publisher : Universitas Pesantren Tinggi Darul 'Ulum (Unipdu) Jombang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/teknologi.v11i2.2402

Abstract

People's activities that often upload photos of food before eating are common in popular social media, such as Facebook, Instagram, Twitter, and Pinterest. The food image data circulating on social media can be used for business purposes, such as analyzing customer behavior patterns. However, not all of the uploaded images are food images, so that before the analysis is carried out, it is necessary to do a classification task between food images and non-food images. Therefore, the researcher proposes automatic food image classification using transfer learning method using the Residual Network model version of 18 (ResNet-18). Residual Network model is used because it has a residual connection mechanism to solve the vanishing gradient problem. In addition, transfer learning was chosen because this method leverages the features and weights that have been generated in the previous training process on large and more general data (Imagenet) and thus reduce computation time and increase accuracy. The test was carried out by comparing the capabilities of the ResNet18 model with AlexNet. In addition, the fine tuning and freeze layer methods used to improve the quality of the model were also carried out in this study. In the experiment, the data set was divided into 3,000 images for training data and 1,000 images for test data, while the evaluation used was correctness accuracy. The results obtained in the ResNet18 model, namely the fine tuning training method, produced an accuracy value of 0.981 while the freeze layer resulted in the best accuracy value of 0.988. The AlexNet model that uses the fine tuning training method produces an accuracy value of 0.970 while the freeze layer produces the best accuracy value of 0.978. It can be concluded that the mechanism with the best accuracy is found in the RestNet18 architecture using the freeze layer 1-3 with an accuracy of 0.988.
Analisis faktor-faktor yang berpengaruh terhadap minat penggunaan e-commerce: Studi kasus di Shopee Indonesia Kurniawan, Ricky Andi; Chendra, Mikhael; Kelvin, Kelvin; Anderson, Kevin; Yudianto, Wahyu
TEKNOLOGI: Jurnal Ilmiah Sistem Informasi Vol 11, No 2 (2021): July
Publisher : Universitas Pesantren Tinggi Darul 'Ulum (Unipdu) Jombang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/teknologi.v11i2.2408

Abstract

The purpose of this study is to analyze the effect of perceived usefulness, perceived ease of use, risk, trust, and attitud e toward using on interest in using the Shopee application. The types that we use for research are qualitative and quantitative. This research was conducted with a number of respondents as many as 168 respondents. The sampling technique used was the cluster & stratified, dispropotional, random sampling technique. Hypothesis testing using multiple linear regression. The results showed that trust in conducting transactions and behavior in transactions can affect the interest in using the Shopee application in Batam city and other factors do not affect people's decisions in the city of Batam. The test results of the coefficient of determination on the adjusted R2 value show a value of 0.56 which means the independent variables in the study show a value of 56% and the other 44% is explained by the outside variables. This research can be a reference for readers in determining how to attract users to use or transact at Shopee in the city of Batam.
Analisis faktor-faktor yang berpengaruh terhadap minat penggunaan e-commerce: Studi kasus di Shopee Indonesia Kurniawan, Ricky Andi; Chendra, Mikhael; Kelvin, Kelvin; Anderson, Kevin; Yudianto, Wahyu
TEKNOLOGI: Jurnal Ilmiah Sistem Informasi Vol 11, No 2 (2021): July
Publisher : Universitas Pesantren Tinggi Darul 'Ulum (Unipdu) Jombang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/teknologi.v11i2.2408

Abstract

The purpose of this study is to analyze the effect of perceived usefulness, perceived ease of use, risk, trust, and attitud e toward using on interest in using the Shopee application. The types that we use for research are qualitative and quantitative. This research was conducted with a number of respondents as many as 168 respondents. The sampling technique used was the cluster & stratified, dispropotional, random sampling technique. Hypothesis testing using multiple linear regression. The results showed that trust in conducting transactions and behavior in transactions can affect the interest in using the Shopee application in Batam city and other factors do not affect people's decisions in the city of Batam. The test results of the coefficient of determination on the adjusted R2 value show a value of 0.56 which means the independent variables in the study show a value of 56% and the other 44% is explained by the outside variables. This research can be a reference for readers in determining how to attract users to use or transact at Shopee in the city of Batam.

Page 1 of 1 | Total Record : 10