Article Per Year (5 Year)
p-Index From 2020 - 2025
2.534
P-Index
This Author published in this journals
All Journal SITEKIN: Jurnal Sains, Teknologi dan Industri Prosiding Semnastek Jurnal Teknologi dan Sistem Komputer Indonesian Journal of Artificial Intelligence and Data Mining Seminar Nasional Teknologi Informasi Komunikasi dan Industri Jurnal Informatika Universitas Pamulang JURIKOM (Jurnal Riset Komputer) Informatika : Jurnal Informatika, Manajemen dan Komputer Jurnal Tekinkom (Teknik Informasi dan Komputer) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) JUKI : Jurnal Komputer dan Informatika KLIK: Kajian Ilmiah Informatika dan Komputer
Lestari Handayani
Fakultas Sains Dan Teknologi Universitas Islam Negeri (UIN) Sultan Syarif Kasim Riau
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mathematics Other

Published : 32 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 32 Documents
Search

 1   2   3   4 
Identifikasi Area Kanker Ovarium pada Citra CT Scan Abdomen Menggunakan Metode Expectation Maximization Lestari Handayani
Seminar Nasional Teknologi Informasi Komunikasi dan Industri 2012: SNTIKI 4
Publisher : UIN Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (541.26 KB)

Abstract

Ovarian Cancer is a deadly disease, because the patient is too late to be aware of this diseaseand come late to treatment. To detect the condition of patient, it’s need examination such as USG withDoppler, CT scan abdomen or MRI. The examination cannot used to diagnose ovarian cancer, but only todo operation. Therefore, we need systems to analyze of this condition. One part of the systems is how toidentify area of cancer. In this paper, we use image from ct scan examination result. The methodExpectation Maximization with Gaussian Mixture Model (EM GMM) is used to segmentation of ovariancancer areas. The experiment result is EM-GMM method can separate image into some classificationbased on pixel feature, even though not so good to distinguish area of cancer and not cancer. It’s seen fromthe results of calculation of the percentage of pixels that estimated cancer or not, the value of TP(True Positive) is45%, while FP(False Positive) is 55%. It caused both of them are same in pixel value. To improve the result,we need another feature to segmentation, for example is shape feature.Keywords: CT scan Abdomen, Expectation Maximization, Gaussian Mixture Model, Ovarian Cancer.
Rancang Bangun Aplikasi Prediksi Kebiasaan Pelanggan dengan Metode Association Rule Mining (ARM) (Studi Kasus Perusahaan Digital Printing) Lestari Handayani; Iwan Iskandar; Gatot Suroto
Seminar Nasional Teknologi Informasi Komunikasi dan Industri 2016: SNTIKI 8
Publisher : UIN Sultan Syarif Kasim Riau

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

Abstract

Availability of detailed data customer transactions is the largest operation undertaken by a digital printing company. Digital printing company should have the customer database. The problem of company to keep consumer loyalty is how to make prediction of customer habits that appropriate with type and target customer market. This system is a system prediction of customer habits that built using the Association Rule Mining Method. Association mining is method mining technique to find the rules of assosiative between combination itemset, the calculation is done by determining the value of minimum support and minimum confidence. The result of best rule calculation used as a combination product recommendations that offered to customers when the transaction took place and can use to reference in making promo and catalog. This system was built using Microsoft Visual Basic.Net and database Microsoft Access. This system can be used as a solution for company to make prediction of customer habits by type and target customer market, that can be help the company to increasce corporate image and profit the company. This study used best testing rule with minimum support 30% and minimum confidence 50%.Keywords : ARM, combination, digital printing, itemset, rule, minimum support, minimum confidence, prediction.
Pwenerapan JST(Backpropagation) untuk Prediksi curah hujan (Studi kasus: Kota Pekanbaru) Lestari Handayani; Muhammad Adri
Seminar Nasional Teknologi Informasi Komunikasi dan Industri 2015: SNTIKI 7
Publisher : UIN Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (427.149 KB)

Abstract

Weather forecasting is an important thing in our lives and can help us to minimize the impact it will have in the future, we need a very high accuracy in forecasting the weather conditions in the future. For that, he built a system that can predict the weather. In this study using an artificial neural network models are often used in forecasting. The method used is Backpropagation where there is a training process the data (making patterns), forecasting and output in the form of a state system of rainfall and its weight. In this study using binary sigmoid activation function (logsig) and bipolar sigmoid (tansig) using parameters epoch 1000, learning rate 0.01 and error 0.001. The data used from the city of Pekanbaru BMKG station with focus testing system for local area Pekanbaru. Output adjusted into 5 categories: bright, light rain, moderate rain, heavy rain and torrential rain by BMKG standards. An accuracy system is 96 %, where most of the failures are in the category of moderate rain and heavy rain.Keywords: Backpropagation, BMKG, Pekanbaru, Weather Forecast
Implementasi Learning Vektor Quantization (LVQ) dalam Mengidentifikasi Citra Daging Babi dan Daging Sapi Jasril Jasril; Meiky Surya Cahyana; Lestari Handayani; Elvia Budianita
Seminar Nasional Teknologi Informasi Komunikasi dan Industri 2015: SNTIKI 7
Publisher : UIN Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (586.668 KB)

Abstract

Widespread circulation of adulterated meat and based on the word of Allah which confirms the prohibition of pork to eat, it needs to be made of a system that can distinguish between beef and pork to avoid cheating merchants and keep halal meat we eat. This study makes a system for identifying the image of beef and pork and meat adulterated with the color feature extraction HSV (Hue, Saturation, Value) and texture feature extraction GLCM (Grey Level Co-occurent Matrix) using classification LVQ (Learning Vector Quantization). A result of image identification adulterated meat pig is considered as a pork class. Image data on the image of the study consisted of 107 primary and 13 secondary image. Identification testing conducted on the distribution of training data and test data are different. Accuracy of the highest success with an average of 94.81% on the distribution of the 80 training data and test data 20 and the accuracy of the lowest success with an average of 82.22% on the distribution of training data and test data 50 50 with Learning Rate of 0.01, 0.05, 0.09. More increase the distribution of training data and more decrease division of the test data, so more increase the accuracy of success in identifying the image.Keywords: beef, GLCM, HSV, Learning Rate, LVQ, pork
Sistem Informasi Geografis Untuk Menentukan Lokasi Pengembangan Pemukiman Menggunakan Metode Perbandingan Eksponensial Lestari Handayani; Muhammad Fikry; Rendra Arga Swaperi
Seminar Nasional Teknologi Informasi Komunikasi dan Industri 2016: SNTIKI 8
Publisher : UIN Sultan Syarif Kasim Riau

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

Abstract

Pekanbaru is one of cities in Indonesia that is growing. Nit only physically evolved from the development side, the city of Pekanbaru also experiencing population growth. Population growth in the city of Pekanbaru will certainly pose new problems of residential land needs. The uneven distribution og the population in each district requires a solution to new settlements. To be able to determine the location of residential development designed a Geographic Information System (GIS). This system will calculate the feasibility of land to be used as residential areas by using the Comparative Method of Exponential (CME). Then this system will show the calculation results in the form of digital maps. This system has a feature to determine the public facilities of the nearby vacant land in each alternative location. This system uses administrative boundary map of Riau Province in 2005 and use the research data in 2005. In addition, this system can save the calculation results in a database, so it can be used without the need to run the function again CME. Keywords : Geographic Information System, population distribution, settlement
Klasifikasi Tingkat Keberhasilan Produksi Ayam Broiler di Riau Menggunakan Algoritma C4.5 Muhammad Rifaldo Al Magribi; Alwis Nazir; Siska Kurnia Gusti; Lestari Handayani; Iwan Iskandar
JURIKOM (Jurnal Riset Komputer) Vol 10, No 1 (2023): Februari 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v10i1.5496

Abstract

Broiler chicken farming is one sector that contributes to playing an important role in causing an increase in the quality of life of the community, especially in fulfilling animal protein. Broiler chicken is a superior breed that has high meat productivity and a short reproductive cycle, thus encouraging the formation of partnerships between breeders and large companies. As the core, the company evaluates the success of breeders as seen from the performance index or IP value. The attributes that affect the IP value are depletion, average harvest weight, feed conversion ratio (FCR), and harvest age. The purpose of this research is to find out the attributes that most influence the success rate of broiler production in Riau and to get the accuracy value of the decision tree model using the C4.5 algorithm. This study used 952 livestock production data in Riau divided by a ratio of 80% training data and 20% test data. This test produces a decision tree in which the FCR attribute is the root node with a gain value of 0.45 and is the attribute that most influences the success rate of broiler chicken production in Riau. Evaluation using the confusion matrix produces an accuracy value of 97.11%, a precision of 98.89%, a recall of 98.16%.
Klasifikasi Tingkat Keberhasilan Produksi Ayam Broiler di Riau Menggunakan Algoritma K-Nearest Neighbor Beni Basuki; Alwis Nazir; Siska Kurnia Gusti; Lestari Handayani; Iwan Iskandar
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 3 (2023): Maret 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i3.5800

Abstract

Livestock is a crucial component of the Indonesian agriculture sector. One of the most widely practiced types of livestock farming is broiler chicken farming. The production of broiler chickens continues to increase due to the increasing consumption of broiler chickens. Presently, companies are facing an urgent requirement to support farmers, regardless of their level of experience, whether they are newly entering the sector or have been established for some time. Core companies encounter challenges in modeling the success rate of broiler chicken farmer production because of the vast quantity of data coming from collaborating farmers, which makes it arduous for the company to establish the success rate of broiler chicken production. Establishing the level of production success is very helpful in selecting the appropriate farmers to be guided, thus enabling accurate decision-making. A classification procedure utilizing data mining and K-Nearest Neighbor (KNN) algorithm is necessary to manage the growing volume of data. The study examined 927 livestock production data from Riau, where the data was divided into two sets, with 80% allocated for training and the remaining 20% for testing purposes. The findings of the confusion matrix analysis showed that the optimal result was achieved at k = 3, with an accuracy rate of 86.49%, precision of 75.00%, and recall of 70.21%.
Penerapan Algoritma K-Medoids Clustering untuk Mengetahui Pola Penerima Beasiswa Bank Indonesia Provinsi Riau Mentari Aulia Putri; Alwis Nazir; Lestari Handayani; Iis Afrianty
JUKI : Jurnal Komputer dan Informatika Vol. 5 No. 1 (2023): JUKI : Jurnal Komputer dan Informatika, Edisi Mei 2023
Publisher : Yayasan Kita Menulis

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

Abstract

Beasiswa Bank Indonesia merupakan bentuk dukungan finansial yang diberikan Bank Indonesia untuk para mahasiswa di Perguruan Tinggi Negeri dan Swasta terpilih. Melihat data penerima yang diperoleh sejak tiga tahun terakhir yakni 2020, 2021 dan 2022, perlu dilakukan pencarian pola karakteristik penerima beasiswa Bank Indonesia dikarenakan pihak Bank Indonesia belum memiliki pola. Metode yang digunakan dalam penelitian ini adalah metode data mining yang menggunakan algoritma K-Medoids dan aplikasi rapidminer. Data diperoleh bersumber dari Humas Bank Indonesia berupa data penerima beasiswa tahun 2020,2021, dan 2022. Variabel yang digunakan dalam penelitian ini yaitu IPK, Program Studi, dan Semester. Hasil dari penelitian ini yaitu pada Sekolah Tinggi Teknologi Dumai penerima beasiswa didominasi oleh mahasiswa Teknik Informatika. Pada UIN Suska Riau, penerima beasiswa didominasi oleh mahasiswa program studi Ilmu Komunikasi dan Fakultas Pertanian Peternakan semester 7. Pada Universitas Riau penerima beasiswa didominasi oleh mahasiswa dari Fakultas Ilmu Sosial dan Politik, Fakultas Ekonomi dan bisnis, program studi Pendidikan Ekonomi, Program Studi Ilmu Hukum semester 7. Pada Universitas Lancang Kuning didominasi oleh mahasiswa Fakultas Ekonomi dan Bisnis sesmeter 7. Pada Universitas Muhammadiyah Riau beasiswa ini didominasi oleh mahasiswa Program Studi Sistem Informasi semester 7. Kemudian juga dapat dilihat bahwa mahasiswa penerima beasiswa Bank Indonesia di tiap Universitas didominasi oleh mahasiswa yang memiliki IPK besar dari 3.5.
Implementasi Triple Exponential Smoothing dan Double Moving Average Untuk Peramalan Produksi Kernel Kelapa Sawit Risfi Ayu Sandika; Siska Kurnia Gusti; Lestari Handayani; Siti Ramadhani
Journal of Information System Research (JOSH) Vol 4 No 3 (2023): April 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i3.3359

Abstract

The production of palm kernel is a significant product for the company and plays a crucial role. Nevertheless, the stability of kernel production is not always consistent, and the quality of the kernel can be detrimental to the company. As consumer demands change over time, companies must anticipate every fluctuation in palm kernel production. Hence it is vital to figure the long run with a settlement prepare utilizing information mining utilizing information within the past. The Triple Exponential Smoothing and Double Moving Average methods, which are data mining methods for future forecasting, were used in this study. The aim of this research is to predict the yield of future oil palm kernel production using the Triple Exponential Smoothing and Double Moving Average methods and to determine the level of forecasting errors using the Mean Absolute Percentage Error (MAPE) method. The data for the last ten years, from January 2013 to December 2022, were used in this study. After testing the Triple Exponential Smoothing method with parameters α=0.2,β=0.γ=0.2, the error rate using MAPE was 9.48%, and the Double Moving Average method had an error rate of 11.2%. The MAPE results of the Triple Exponential Smoothing method are considered very good, while the MAPE results of the Double Moving Average method are categorized as good based on the range of MAPE values. This research is expected to provide information to related companies as a supporting reference in anticipating palm oil kernel production. The conclusion of the research is that the Triple Exponential Smoothing method with the test parameters is the best method for forecasting.
Penerapan Algoritma Fuzzy C-Means untuk Melihat Pola Penerima Beasiswa Bank Indonesia Agung Surya Maulana; Alwis Nazir; Lestari Handayani; Iis Afrianty
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 3 No. 6 (2023): Juni 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v3i6.788

Abstract

Scholarship is a program in the form of financial assistance aimed at individuals to continue their education with the aim of helping reduce the financial burden during the study period, especially in difficult situations, so that it can help expedite the learning process. Based on data related to scholarship recipients obtained in 2020, 2021 and 2022, analysis is needed to see the characteristics of Bank Indonesia scholarship recipients because Bank Indonesia does not yet know this, this was said directly by the Bank Indonesia Scholarship supervisor. The method needed for grouping data is data mining with the Fuzzy C-Means algorithm and using a computerized system, namely the RapidMiner application. This study uses the Cumulative Grade Point Average (GPA), Semester, and Study Program variables. The research results obtained were at Riau University for three years, the pattern formed was students of the Faculty of Social and Political Sciences with a large GPA of 3.5. At Sultan Syarif Kasim Riau State University, Riau Muhammadiyah University, and Lancang Kuning University have the same pattern, namely students with a GPA above 3.5. Then at the Dumai College of Technology, namely Informatics Engineering students with a large GPA of 3.5
Co-Authors Agung Surya Maulana Ahmad Paisal Alfin Hernandes Alwis Nazir Alwis Nazir Alwis Nazir Anggi Vasella Angraini Angraini Beni Basuki Cut Lira Kabaatun Nisa Deny Ardianto Eka Lona Maulida Elvia Budianita Elvia Budianita Elvia Budianita Fachrul Hakim Fadhilah Syafria Fadhilah Syafria Febi Yanto Febi Yanto Fitriandini Fitriandini Gatot Suroto Hafez Almirza Iis Afrianty Iis Afrianty Ikhwanul Akhmad DLY Indah Inzani Septa Iwan Iskandar Iwan Iskandar Iwan Iskandar Jasril Jasril Jasril Jasril Jasril Jasril Jasril Jasril Jasril Liza Afriyanti M Irsyad M. Fadil Martias Meiky Surya Cahyana Mentari Aulia Putri Mohd. Ridho Zarkasih Rahim Muhammad Adri Muhammad Fikry Muhammad Fikry Muhammad Rifaldo Al Magribi Mustakim Mustakim Nazruddin Safaat H Nuari Ananda Okfalisa Okfalisa Qurrata A'yuni Raja Indra Ramoza Rendra Arga Swaperi Risfi Ayu Sandika Rusnedy, Hidayati Shella Novi Siska Kurnia Gusti Siska Kurnia Gusti Siska Kurnia Gusti Siti Ramadhani Siti Ramadhani Suwanto Sanjaya Suwanto Sanjaya Suwanto Sanjaya Ulfah Adzkia Yusra, Yusra