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Estimated Economic Growth Rate Based on Efek Decrease in PPKM Level Using Support Vector Regression Method Daniel Setyo Cahyo Utomo; Adi Nugroho
Journal of Information System and Informatics Vol 4 No 2 (2022): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v4i2.246

Economic growth is a change in economic conditions towards a better level. An increase in the value of income and production will affect the condition of a country. It can be known that the impact of Covid-19 is enough to affect economic conditions in Indonesia. Economic conditions in Indonesia are also affected by the PPKM program from the government. The bill of decreasing the PPKM level that is applied is considered to provide an increase in the economy towards normal and better. The purpose of this study is to provide predictions and analysis of the value of future economic growth. The method used is SVR (Support Vector Regression). This method is processed using a Polynomial kernel and using MAPE (Mean Absolute Percentage Error) error accuracy. Based on research that has been carried out, the results of the value of each economic nit in the fourth quarter of 2021 and the first to fourth quarters of 2022 with a MAPE value of 3.6% which is included in the very good category. In this study, an analysis was also obtained that there will be an economic increase of 1.14% along with a decrease in the PPKM level.

ANALISIS SENTIMEN BERBASIS ASPEK PADA ULASAN APLIKASI KAI ACCESS MENGGUNAKAN METODE SUPPORT VECTOR MACHINE Gracia Radiena; Adi Nugroho
Jurnal Pendidikan Teknologi Informasi (JUKANTI) Vol 6 No 1 (2023): Jurnal Pendidikan Teknologi Informasi (JUKANTI) Edisi April 2023
Publisher : Program Studi Pendidikan Informatika, Universitas Citra Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37792/jukanti.v6i1.836

PT Kereta Api Indonesia melakukan inovasi dengan meluncurkan aplikasi yang diberi nama KAI Access. Aplikasi KAI Access memiliki fitur pemesanan tiket, ticket rescheduling, pembatalan tiket hingga e-boarding pass. Tujuan dari penelitian ini adalah untuk mengetahui sentimen dari sebuah produk mobile. Opini terkait Aplikasi KAI Access dapat digunakan PT Kereta Api Indonesia sebagai parameter kunci untuk mengetahui tingkat kepuasan publik sekaligus bahan evaluasi bagi PT Kereta Api Indonesia. Berdasarkan hasil pengujian yang telah dilakukan pada ulasan pengguna aplikasi KAI Access dengan total 8.078 ulasan, lebih banyak pengguna memberikan opini positif dalam aspek satisfaction dan opini negatif pada aspek learnability, efficiency, dan errors. Digunakan model CRISP-DM (Cross Industry Standard Process for Data Mining) dan algoritma Support Vector Machine untuk melakukan klasifikasi. Hasil klasifikasi terbaik diperoleh nilai accuracy, precision, recall, dan F-measure yang dihasilkan dari tiap aspek yaitu untuk Learnability 94.73%, 100.00%, 89.50%, dan 94.64%, Efficiency 94.38%, 72.00%, 100.00%, dan 94.46%, Errors 85.13%, 97.11%, 72.41%, dan 82.96%, Satisfaction 87.26%, 98.46%, 73.78%, dan 84.20%. PT Kereta Api Indonesia innovates by launching an application called KAI Access. The KAI Access application has features for ticket ordering, ticket rescheduling, ticket cancellation and e-boarding pass. The purpose of this study is to determine the sentiment of a mobile. Opinion regarding the KAI Access Application can be used by PT Kereta Api Indonesia as a key parameter to determine the level of public satisfaction as well as evaluation material for PT Kereta Api Indonesia. Based on the results of tests conducted on user reviews of the KAI Access application with a total of 8,078 reviews, more users give positive opinions on the satisfaction and negative opinions on the learnability, efficiency and errors. Model CRISP-DM (Cross Industry Standard Process for Data Mining) and Support Vector Machine algorithm are used to perform classification. The best classification results obtained accuracy, precision, recall, and F-measure resulting from each aspect, namely for Learnability 94.73%, 100.00%, 89.50%, and 94.64%, Efficiency 94.38%, 72.00%, 100.00%, and 94.46%, Errors 85.13%, 97.11%, 72.41%, and 82.96%, Satisfaction 87.26%, 98.46%, 73.78%, and 84.20%.

Analisis Prediksi Mahasiswa Terhadap Kelulusan Tepat Waktu Menggunakan Metode Data Mining Decision Tree (Studi Kasus: FTI UKSW) Imelda Ruwae Lutunani; Adi Nugroho
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 7 No 2 (2023): APRIL-JUNE 2023
Publisher : KITA Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v7i2.781

In general, students have the responsibility to complete their studies at a university. For students of the Satya Wacana Christian University Faculty of Information Technology, which every year there are more and more students, the world of work is currently required to become someone who masters the field of technology. In addition, as a student, there are many things that must be done to complete studies by participating in activities on campus, organizations, and being active in the teaching and learning process so that they can complete their studies on time. In this study, a predictive analysis of SWCU FTI students will be conducted on timely graduation using the decision tree data mining method. which will see students who graduate on time and graduate late using the decision tree algorithm which is a decision tree algorithm that has a high level of accuracy in large amounts of data. In this study, the decision tree algorithm was used to run 983 sample data, resulting in a match accuracy of 91.25%. This means that it is very good and effective in predicting student graduation

PENGAMBILAN KEPUTUSAN DENGAN MENGGUNAKAN METODE SIMPLE ADDITIVE WEIGHTING (SAW) UNTUK MENENTUKAN PEMBELIAN MESIN TEMPEL Chrisandy Noel Siruru; Adi Nugroho
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 2 (2023): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) AMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i2.276

The decision-making system is crucial in business processes to assist decision-makers in making the right decisions. In this case, the determination of the purchase of an outboard motor is a critical decision that requires careful consideration. Therefore, the Simple Additive Weighting (SAW) method is used as a decision-making system in determining the purchase of an outboard motor. The SAW method is used to calculate the relative weight of each selected criterion and to give a value to each alternative based on the given criteria. The criteria used in determining the purchase of an outboard motor include price, brand, features, environmental waste guarantee, quality, and availability of service centers. From the normalized calculation results, Yamaha E40JMHL 40HP became the best outboard motor alternative with a score of 0.8125, and Honda BF 90 DK 2LRTD 0.575 became the less recommended alternative to be chosen for purchase.

ANALISIS SENTIMEN PENILAIAN MASYARAKAT INDONESIA TERHADAP KONFERENSI G20 DI BALI DENGAN MENGGUNAKAN METODE NAIVE BAYES Windy Kusumawati Suhet; Adi Nugroho
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 3 (2023): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) AMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i3.290

Lot of Indonesian people support the holding of the G20 conference in Bali and many people also disagree with the G20 that will held/hold in Bali for various reasons. Therefore this research is conducted to find out how much the Indonesian people agree or disagree with the holding of the G20 High-Level Conference (Summit) which was held in Bali, Indonesia. In this study used research methods of data collection, preprocessing stage, Naive Bayes classification stage, and confusion matrix evaluation stage. In this study, 2,255 data were used with 1,000 training data and 1,255 test data. After the data is processed and the sentiment predicted in the test data, the confusion matrix is calculated to calculate the accuracy of the results. On data that has been processed the accuracy obtained is 95.60%.

ANALISIS KESEHATAN MENTAL MAHASISWA UNIVERSITAS KRISTEN SATYA WACANA MENGGUNAKAN METODE CLUSTERING ALGORITMA K-MEANS Timothy Garry Van Solang; Adi Nugroho
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 6 No 1 (2023)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v6i1.641

Mental health and machine learning technology that are trending among students provide a presentation that mental health awareness and technology use will have an impact in the future. This research aims to provide awareness of Satya Wacana Christian University data about mental health that can be identified using machine learning technology. The use of K-Means Clustering in clustering has been done in various types of research. Mental health scale that can recognize the state felt by Satya Wacana Christian University students based on answers to questions. The answers are in the form of a numeric scale, so the data is used in Orange3 for clustering using the K-Means algorithm. Analysis on the scale data of UKSW students who have 32 data has a silhouette k = 3 in cluster 1 of the depressed category has the results of 11 students seen in the 2018 batch and above in the depressed category and 1 data of 2020 batch students. In cluster 2 has 12 data which has the results of the 2018, 2019 and 2020 generations in the prosperous category. Cluster 3 of the harmonious category has data on 9 students whose classes are various in 2017, 2018 and 2019. The results in each cluster provide an overview of the effect of batch on mental health where many of the early year batches are in the prosperous category then the depressed category with the 3rd year batch and there are students who are able to balance their mental health with harmonious categories scattered in each batch.

Pencarian Rute Terpendek menggunakan Algoritma Genetika (Studi Kasus : Pengoptimalan Mobilitas Kota Salatiga Terhadap Kota-Kota Tetangga) Agustho Isai; Adi Nugroho
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 1 (2024): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) AMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v5i1.538

The increase in population in an area has a significant impact on vehicle density in that area. The problem that often arises is traffic jams that occur on several routes at once. This factor is a common problem, which is quite disturbing to the public and road users. To overcome this problem, the genetic algorithm method is used as a solution method. This case study was set at the regional or city level around Salatiga City, including Ambarawa City, Semarang City, Boyolali City, Solo City and Magelang City. By using the genetic algorithm method, the fastest route with the shortest distance can be found. The Genetic Algorithm used in this research allows optimal shortest route search results. Apart from that, this research can also minimize the distance and travel time of several alternative routes obtained, thus providing significant benefits in developing the transportation system in the region.

ANALISIS SENTIMEN BERBASIS ASPEK PADA ULASAN APLIKASI KAI ACCESS MENGGUNAKAN METODE SUPPORT VECTOR MACHINE Gracia Radiena; Adi Nugroho
Jurnal Pendidikan Teknologi Informasi (JUKANTI) Vol 6 No 1 (2023): Jurnal Pendidikan Teknologi Informasi (JUKANTI) Edisi April 2023
Publisher : Program Studi Pendidikan Informatika, Universitas Citra Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37792/jukanti.v6i1.836

PT Kereta Api Indonesia melakukan inovasi dengan meluncurkan aplikasi yang diberi nama KAI Access. Aplikasi KAI Access memiliki fitur pemesanan tiket, ticket rescheduling, pembatalan tiket hingga e-boarding pass. Tujuan dari penelitian ini adalah untuk mengetahui sentimen dari sebuah produk mobile. Opini terkait Aplikasi KAI Access dapat digunakan PT Kereta Api Indonesia sebagai parameter kunci untuk mengetahui tingkat kepuasan publik sekaligus bahan evaluasi bagi PT Kereta Api Indonesia. Berdasarkan hasil pengujian yang telah dilakukan pada ulasan pengguna aplikasi KAI Access dengan total 8.078 ulasan, lebih banyak pengguna memberikan opini positif dalam aspek satisfaction dan opini negatif pada aspek learnability, efficiency, dan errors. Digunakan model CRISP-DM (Cross Industry Standard Process for Data Mining) dan algoritma Support Vector Machine untuk melakukan klasifikasi. Hasil klasifikasi terbaik diperoleh nilai accuracy, precision, recall, dan F-measure yang dihasilkan dari tiap aspek yaitu untuk Learnability 94.73%, 100.00%, 89.50%, dan 94.64%, Efficiency 94.38%, 72.00%, 100.00%, dan 94.46%, Errors 85.13%, 97.11%, 72.41%, dan 82.96%, Satisfaction 87.26%, 98.46%, 73.78%, dan 84.20%. PT Kereta Api Indonesia innovates by launching an application called KAI Access. The KAI Access application has features for ticket ordering, ticket rescheduling, ticket cancellation and e-boarding pass. The purpose of this study is to determine the sentiment of a mobile. Opinion regarding the KAI Access Application can be used by PT Kereta Api Indonesia as a key parameter to determine the level of public satisfaction as well as evaluation material for PT Kereta Api Indonesia. Based on the results of tests conducted on user reviews of the KAI Access application with a total of 8,078 reviews, more users give positive opinions on the satisfaction and negative opinions on the learnability, efficiency and errors. Model CRISP-DM (Cross Industry Standard Process for Data Mining) and Support Vector Machine algorithm are used to perform classification. The best classification results obtained accuracy, precision, recall, and F-measure resulting from each aspect, namely for Learnability 94.73%, 100.00%, 89.50%, and 94.64%, Efficiency 94.38%, 72.00%, 100.00%, and 94.46%, Errors 85.13%, 97.11%, 72.41%, and 82.96%, Satisfaction 87.26%, 98.46%, 73.78%, and 84.20%.

KLASIFIKASI DATA PENJUALAN UNTUK MEMPREDIKSI TINGKAT PENJUALAN PRODUK MENGGUNAKAN METODE DECISION TREE Demira Intan Suranda; Adi Nugroho
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 7 No 1 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v7i1.1269

A mature strategy is one of the keys so that a company can increase sales effectively and consistently. With recorded and accurate information, companies can make decisions quickly to predict what supplies consumers will need for the future. Aruna Boutique sells various types of Muslim clothing such as robes and headscarves with several brands of each type. The aim of this research is to determine the sales of the best-selling and least-selling products using the Decision Tree method with the ID3 algorithm. The tool used is a rapid miner using boutique sales transaction data from July - September. The results obtained in this research are the best-selling products Gamis 2 (Umama), veil 2 (DYN) and the less popular products Gamis 1 (Mahdani), veil 3 (Azara) with an accuracy value of 88.24%, which means that the method used it's good enough. Based on the rules obtained, information can be used to increase sales in terms of stock inventory, display and promotion strategies.

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