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All Journal Semantik Seminar Nasional Informatika (SEMNASIF) JOIN (Jurnal Online Informatika) Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Komputer Terapan Jurnal Elementer (Elektro dan Mesin Terapan) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Communications in Science and Technology Dinamisia: Jurnal Pengabdian Kepada Masyarakat JURNAL ILMIAH INFORMATIKA ComTech: Computer, Mathematics and Engineering Applications Jurnal Pengabdian Masyarakat Multidisiplin Jurnal Akuntansi Keuangan dan Bisnis Proceeding Applied Business and Engineering Conference Proceeding International Applied Business and Engineering Conference JITER-PM (Jurnal Inovasi Terapan - Pengabdian Masyarakat) Jurnal Informatika Polinema (JIP)
Kartina Diah K. W.
Politeknik Caltex Riau
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Aplikasi Kalkulator Perhitungan Pajak Penghasilan Final Pada UMKM Berbasis Android Tobi Arfan; Kartina Diah Kusuma. W
Jurnal Akuntansi Keuangan dan Bisnis Vol 13 No 2 (2020): Jurnal Akuntansi Keuangan dan Bisnis
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (387.792 KB) | DOI: 10.35143/jakb.v13i2.4343

Abstract

Minimnya kemampuan UMKM dalam melakukan pencatatan keuangan dan membuat laporan keuangan telah menjadi masalah pelik bagi pengusaha UMKM dalam hubungannya dengan perhitungan, pembayaran dan pelaporan pajak. Laporan keuangan juga diperlukan untuk melaksanakan kewajiban pajak. Adanya pelaksanaan sistem self assessment pada sistem perpajakan di Indonesia telah menuntut wajib pajak untuk aktif menghitung, melaporkan dan membayar sendiri jumlah pajak yang terhutang kepada negara. Namun, tidak semua pelaku UMKM paham cara menghitung pajak yang menjadi kewajiban mereka. Apalagi untuk menuntaskan kewajiban perpajakannya seperti membayar dan melaporkan PPh Final. Sejalan dengan pemanfaatan teknologi, penelitian ini memberikan alternative solusi bagi permasalahan pelaku UMKM dengan mengembangkan aplikasi android yang dapat memberikan fungsi dokumentasi transaksi harian yang diperoleh untuk kemudian menghitung PPh Final UMKM yang harus dikeluarkan sesuai dengan besarnya penghasilan pelaku UMKM dari transaksi yang telah didokumentasikan. Aplikasi telah berhasil dikembagkan dan menunjukkan telah memenuhi semua fungsionalitas system 100% berdasarkan pengujian blackbox testing.
Diabetes Risk Prediction Using Extreme Gradient Boosting (XGBoost) Kartina Diah Kusuma Wardhani; Memen Akbar
JOIN (Jurnal Online Informatika) Vol 7 No 2 (2022)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v7i2.970

Abstract

One of the uses of medical data from diabetes patients is to produce models that can be used by medical personnel to predict and identify diabetes in patients. Various techniques are used to be able to provide a diabetes model as early as possible based on the symptoms experienced by diabetic patients, including using machine learning. The machine learning technique used to predict diabetes in this study is extreme gradient boosting (XGBoost). XGBoost is an advanced implementation of gradient boosting along with multiple regularization factors to accurately predict target variables by combining simpler and weaker model set estimations. Errors made by the previous model are tried to be corrected by the next model by adding some weight to the model. The diabetes prediction model using XGBoost is shown in the form of a tree, with the accuracy of the model produced in this study of 98.71%
PENGEMBANGAN APLIKASI E-COMMERCE PADA PRODUK HANDMADE ROTAN MENGGUNAKAN METODOLOGI INCREMENTAL STUDI KASUS: DONA ROTAN RUMBAI chintia romauli rambe Rambe; Kartina Diah Kesuma Wardhani, S.T.,M.T.
ABEC Indonesia Vol. 9 (2021): 9th Applied Business and Engineering Conference
Publisher : Politeknik Caltex Riau

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

Abstract

Dona Rattan shop is a business that makes various products from processed rattan into works of art. The traditional sales system is considered no longer adequate for handling work processes, especially the difficulties experienced by customers in knowing what products are offered at the Dona Rattan Store, and the difficulty of customers who are far from the store to be able to place an order, having to come directly. to the store because the range of promotions is still limited around the tassel. An alternative solution to these problems is the Development of E-commerce Applications Using the Website-based Incremental Methodology and the test results show that all the features or functionality of the existing system are running as expected. Based on the results of the tests carried out, the percentage value of 93.5% (with the category strongly agree) which indicates that the Development of E-commerce Applications Using the Incremental Methodology (Case Study: Toko Dona Rattan) has been running as desired and has been accepted by users. Keywords: Dona Rattan Shop, web, e-Commerce, Incremental Method.
Early Detection Of Alzheimer Disease In Elderly Web-Based Using Support Vector Machine Classification Method Juni Nurma Sari; Syaparudin BS; Kartina Diah KW; Puja Hanifah
International ABEC Vol. 2 (2022): Proceeding International Applied Business and Engineering Conference 2022
Publisher : International ABEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1777.15 KB)

Abstract

Alzheimer's disease is characterized by dimentia diseases that usually begin with a decrease in memory. The number of people in around the world with dimentia diseases is estimated to reach 47.5 million and is increased to quadruple by 2050. The risk factors that make someone exposed Alzheimer's disease are aging, alcohol consumption, anterosclerosis, diabetes mellitus, down syndrome, genetics, hypertension, depression, and smoking. Aging is the biggest risk factor for Alzheimer's disease. People with age 65 years and over have a higher risk. Therefore, it is important to early detect Alzheimer's disease in order to start planning adequate care and medical needs. This study aims to create a web-based system for early detection of Alzheimer's disease in the elderly using support vector machine classification. Detection of Alzheimer's disease using the metric Mini Mental State Examination (MMSE) and Clinical Dementia Rating (CDR) obtained through questionnaires to find out about cognitive function, thinking ability and ability to perform daily tasks. Classification is carried out using the Support Vector Machine (SVM) algorithm. Alzheimer's classification testing uses a confusion matrix with an accuracy value of 85%. For system testing carried out User Acceptance Test with general practitioner, the results were obtained that all the features and functions of the system had run as expected.
Edukasi Dasar Basis Data Pada Mata Pelajaran Informatika Siswa MAN 2 Pekanbaru Kartina Diah; Yuliska Yuliska; Khairul Umam Syaliman; Meilany Dewi; Ardianto Wibowo
JITER-PM (Jurnal Inovasi Terapan - Pengabdian Masyarakat) Vol. 1 No. 1 (2023): JITER-PM
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (400.287 KB) | DOI: 10.35143/jiterpm.v1i1.5893

Abstract

MAN 2 Pekanbaru merupakan salah satu sekolah menengah atas unggulan yang ada di Pekanbaru. Dalam rangka peningkatan kemampuan IT kepada para siswa, MAN 2 Pekanbaru menyelenggarakan pembelajaran Informatika sebagai mata pelajaran pilihan. Namun MAN 2 Pekanbaru kesulitan mencari tenaga pengajar yang memiliki kompetensi sesuai dengan kebutuhan mata pelajaran khususnya Basis Data dan tidak tersedia SDM (Guru) yang benar-benar siap untuk memberikan materi mengenai basis data. Berdasarkan permasalahan tersebut, didukung dengan ketersediaan sumber daya di Program Studi Teknik Informatika (PSTI) khususnya Dosen dengan kompetensi Basis Data, maka PSTI mengusulkan program kegiatan pelatihan Dasar Basis Data bagi siswa MAN 2 Pekanbaru untuk kelas X yang berjumlah 9 kelas. Diharapkan setelah pelatihan ini selesai para siswa dapat lebih memahami tentang basis data sehingga kedepannya mampu merancang skema basis data dengan tepat.
Diabetes Risk Prediction using Feature Importance Extreme Gradient Boosting (XGBoost) Kartina Diah Kusuma Wardani; Memen Akbar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 4 (2023): August 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i4.4651

Abstract

Diabetes results from impaired pancreatic function as a producer of insulin and glucagon hormones, which regulate glucose levels in the blood. People with diabetes today are not only experienced adults, but pre-diabetes has been identified since the age of children and adolescents. Early prediction of diabetes can make it easier for doctors and patients to intervene as soon as possible so that the risk of complications can be reduced. One of the uses of medical data from diabetes patients is to produce a model that medical personnel can use to predict and identify diabetes in patients. Various techniques are used to provide the earliest possible prediction of diabetes based on the symptoms experienced by diabetic patients, including the use of machine learning. People can use machine learning to generate models based on historical data from diabetic patients, and predictions are made with the model. In this study, extreme gradient boosting is the machine learning technique for predicting diabetes (xgboost) using XGBoost with importance features. The diabetes dataset used in this study comes from the early stage diabetes risk prediction dataset published by UCI Machine Learning, which has 520 records and 16 attributes. The diabetes prediction model using xgboost is displayed as a tree. The model precision result in this study was 98.71%, for the F1 score was 98.18%. The accuracy obtained based on the best 10 attributes using the importance of the XGBoost feature is 98.72%.
Ekstraksi Click Stream Data Web E-Commerce Menggunakan Web Usage Mining Kartina Diah Kusuma Wardani
Jurnal Informatika Polinema Vol. 7 No. 2 (2021): Vol 7 No 2 (2021)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v7i2.538

Abstract

E-Commerce berkembang pesat dalam world wide web hingga menghasilkan berbagai jenis data yang dapat dianalisa lebih lanjut untuk berbagai keperluan seperti personifikasi web, profiling customer, dan sebagainya. Salah satu jenis data yang dihasilkan e-Commerce adalah click stream data web yang merekam aktivitas visitor web dalam bentuk log data selama berinteraksi pada laman web. Penelitian ini mengekstraksi click stream data web e-commerce untuk mendapatkan pola interaksi konsumen terhadap halaman web selama mengunjungi web e-commerce. Berdasarkan jenis data yang diekstrak maka web usage mining digunakan untuk ekstraksi pola dari click stream data yang berbentuk log data. Teknik mining yang dianalisa terhadap log data e-commerce pada penelitian ini terdiri dari frequent itemset, asociation rules, dan frequence sequence mining. Frequent itemset menghasilkan halaman web yang paling sering diakses oleh visitor. Association rules menghasilkan pola kemungkinan halaman web yang akan diakses visitor jika visitor mengakses halaman-halamn tertentu. Frequence sequence mining mendapatkan pola urutan halaman web yang paling sering diakses oleh visitor web e-commerce saat berinteraksi pada laman web. Pola urutan halaman yang diakses visitor menunjukkan urutan kebiasaan visitor mengunjungi e-commerce. Sedangkan teknik mining yang diimplementasikan untuk menghasilkan pola akses visitor pada penelitian ini adalah Frequence sequence mining. Hasil ekstraksi dari penelitian ini menunjukkan ada enam halaman web yang paling sering diakses oleh konsumen dengan berbagai pola urutan aksesnya.
Diabetes Risk Prediction Using Extreme Gradient Boosting (XGBoost) Kartina Diah Kusuma Wardhani; Memen Akbar
JOIN (Jurnal Online Informatika) Vol 7 No 2 (2022)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v7i2.970

Abstract

One of the uses of medical data from diabetes patients is to produce models that can be used by medical personnel to predict and identify diabetes in patients. Various techniques are used to be able to provide a diabetes model as early as possible based on the symptoms experienced by diabetic patients, including using machine learning. The machine learning technique used to predict diabetes in this study is extreme gradient boosting (XGBoost). XGBoost is an advanced implementation of gradient boosting along with multiple regularization factors to accurately predict target variables by combining simpler and weaker model set estimations. Errors made by the previous model are tried to be corrected by the next model by adding some weight to the model. The diabetes prediction model using XGBoost is shown in the form of a tree, with the accuracy of the model produced in this study of 98.71%
K-Means Clustering to Identity Twitter Build Operate Transfer (BOT) on Influential Accounts M. Khairul Anam; Ike Yunia Pasa; Kartina Diah Kusuma Wardhani; Lusiana Efrizoni; Muhammad Bambang Firdaus
ComTech: Computer, Mathematics and Engineering Applications Vol. 14 No. 2 (2023): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v14i2.10620

Abstract

Twitter is a popular social media with hundreds of millions of users, but some are not human. About 48 million accounts are created by Build Operate Transfer (BOT), which represents up to 15% of all accounts. BOTs are created for various purposes, one of which is to post information about news automatically. However, BOTs have also been abused, such as spreading hoaxes or influencing public perception of a topic. The research aimed to determine which Twitter accounts were identified as BOT accounts based on predefined attributes. The research used tweet data from 213 Twitter accounts. The accounts used as test data were accounts that had influence. After that, the data were clustered using k-means using the attributes of retweets + replies count, followers count, account age, friends count, status count, digits count in name, username length, name similarity, name ratio, and likes count. The results show the optimal number of clustering at k = 3 on the Sum of Squared Errors (SSE) evaluation and the Elbow method and the best quality and cluster power at k = 2 on the silhouette coefficient. It shows that the clustered accounts with the highest number of members on each attribute are places for accounts with high BOT scores from several aspects of the BOT score type.
Heuristics Miner for E-Commerce Visitor Access Pattern Representation Wardhani, Kartina Diah Kesuma; Yunanto, Wawan
Communications in Science and Technology Vol 2 No 1 (2017)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.2.1.2017.21

Abstract

E-commerce click stream data can form a certain pattern that describe visitor behavior while surfing the e-commerce website. This pattern can be used to initiate a design to determine alternative access sequence on the website. This research use heuristic miner algorithm to determine the pattern. σ-Algorithm and Genetic Mining are methods used for pattern recognition with frequent sequence item set approach. Heuristic Miner is an evolved form of those methods. σ-Algorithm assume that an activity in a website, that has been recorded in the data log, is a complete sequence from start to finish, without any tolerance to incomplete data or data with noise. On the other hand, Genetic Mining is a method that tolerate incomplete data or data with noise, so it can generate a more detailed e-commerce visitor access pattern. In this study, the same sequence of events obtained from six-generated patterns. The resulting pattern of visitor access is that visitors are often access the home page and then the product category page or the home page and then the full text search page.
Co-Authors Akbar, Memen Alfin H Anam, M Khairul Ardianto Wibowo Ari Azhar Cakra Ramadhana chintia romauli rambe Rambe Dhimas Sena Rahmantara Efrizoni, Lusiana Heri Subagiyo Ike Yunia Pasa Jan Alif Kreshna Juni Nurma Sari Khairul Umam Syaliman Maksum Ro’is Adin Saf Maksum Ro’is Adin Saf Mardhiah Fadhli, Mardhiah Memen Akbar Muhammad Bambang Firdaus Nurmalasari, Dini Puja Hanifah Sri Devi Anjasmara Sugeng Purwantoro E.S.G.S Surya, Ibnu Syaparudin BS Tisa Magrisa Tobi Arfan Wawan Yunanto Wenda Novayani Weslie Weslie Yohana Dewi Lulu Widyasari Yuli Fitrisia Yuliska Yuliska