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Micro, Small, and Medium Enterprises Eligibility and Financial Institutions Selection for Provision Capital Sutanto, Yusuf; Purnama, Bambang Eka; Rapiyanta , Paulus Tofan
International Journal of Business, Law, and Education Vol. 6 No. 2 (2025): International Journal of Business, Law, and Education
Publisher : IJBLE Scientific Publications Community Inc.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56442/ijble.v6i2.1171

Some of the obstacles to Micro, Small, and Medium Enterprises (MSMEs) existence include difficulty in obtaining additional capital from banking institutions due to lack of employee knowledge and unfulfilled requirements. This research purpose is to determine MSMEs feasibility and selection of appropriate financial institutions to apply for additional capital using a decision support system. There are 25 MSMEs samples in Karanganyar City to be used as research material. Decision tree algorithm is used to calculate initial decisions in specify MSMEs suitability to be given capital. AHP method is used as final decision to decide an appropriate financial institution to carry out additional capital. Accuracy level testing decision tree algorithm implementation to determine MSMEs feasibility resulted in 86.67%. Accuracy level of testing AHP method to decide financial institutions suitability resulted in 76.91%. From the test results, it can be concluded that developed system is good or accurate.

Pelatihan dan Pendampingan Peningkatan Strategi Pemasaran Produk Kue Klepon sebagai Ikon Jajan Pasar di Kota Surakarta Wardhana, Galih Wisnu; Nugroho, Anggoro Panji; Sutanto, Yusuf
WASANA NYATA Vol 9, No 1 (2025)
Publisher : STIE AUB Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36587/wasananyata.v9i1.1986

Kue klepon merupakan salah satu jajanan tradisional yang memiliki potensi besar untuk dikembangkan sebagai ikon kuliner daerah. Namun, pelaku usaha masih menghadapi kendala seperti strategi pemasaran konvensional, kemasan produk kurang menarik, dan minimnya pemanfaatan media digital. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan keterampilan pemasaran digital, memperbaiki desain kemasan, serta memperluas jangkauan pasar produk kue klepon. Metode pelaksanaan meliputi pelatihan strategi pemasaran digital, pendampingan pembuatan konten promosi, dan perancangan desain kemasan yang modern dan higienis. Kegiatan dilaksanakan selama tiga hari, diikuti 20 pelaku UMKM kue klepon di wilayah binaan Kota Surakarta. Hasil kegiatan menunjukkan adanya peningkatan pengetahuan peserta dalam memanfaatkan media sosial untuk promosi, peningkatan kualitas desain kemasan, dan bertambahnya jumlah pesanan setelah promosi digital dilakukan. Kegiatan ini berkontribusi positif pada peningkatan daya saing UMKM dan pelestarian kuliner tradisional.

Extreme Learning Machine Method Application to Forecasting Coffee Beverage Sales Sutanto, Yusuf; Setyadi, Heribertus Ary; Nugroho, Wawan; Al Amin, Budi
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10465

Sales estimates can be used to set product prices and increase expected profits. Flyover coffee shop Karanganyar does not have a methodical forecasting method to estimate and predict their need/demand for coffee beverage products. Two previous research that used Extreme Learning Machine (ELM) method in other predictions stated that ELM method has high accuracy and fast compilation time. Another research predicted jeans sales using the ARIMA model and produced an accuracy of 17.05% based on the MAPE (Mean Absolute Percentage Error) method. Menstrual cycle prediction using the Long Short-Term Memory (LSTM) method produces a MAPE value of 7.5%. Two advantages of ELM method from two previous research were used as the basis for selecting ELM method used in our study. To help predict sales of coffee beverage menus, this research utilized an artificial neural network method using ELM algorithm. ELM method consists of an input layer and an output layer connected through a hidden layer. Data used for the test was daily sales data for a month. Data used for this study consisted of 215 data samples. Daily sales data at the Flyover coffee shop were collected from June to December 2024. Based on the results and analysis of error values using MAPE method, an average error value was 8.274%. From comparison of original data results and prediction data, an average MAPE error value the best number of features and hidden neurons is 5.65%.

Pelatihan dan Pendampingan Peningkatan Strategi Pemasaran Produk Kue Klepon sebagai Ikon Jajan Pasar di Kota Surakarta Wardhana, Galih Wisnu; Nugroho, Anggoro Panji; Sutanto, Yusuf
WASANA NYATA Vol 9, No 1 (2025)
Publisher : STIE AUB Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36587/wasananyata.v9i1.1986

Kue klepon merupakan salah satu jajanan tradisional yang memiliki potensi besar untuk dikembangkan sebagai ikon kuliner daerah. Namun, pelaku usaha masih menghadapi kendala seperti strategi pemasaran konvensional, kemasan produk kurang menarik, dan minimnya pemanfaatan media digital. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan keterampilan pemasaran digital, memperbaiki desain kemasan, serta memperluas jangkauan pasar produk kue klepon. Metode pelaksanaan meliputi pelatihan strategi pemasaran digital, pendampingan pembuatan konten promosi, dan perancangan desain kemasan yang modern dan higienis. Kegiatan dilaksanakan selama tiga hari, diikuti 20 pelaku UMKM kue klepon di wilayah binaan Kota Surakarta. Hasil kegiatan menunjukkan adanya peningkatan pengetahuan peserta dalam memanfaatkan media sosial untuk promosi, peningkatan kualitas desain kemasan, dan bertambahnya jumlah pesanan setelah promosi digital dilakukan. Kegiatan ini berkontribusi positif pada peningkatan daya saing UMKM dan pelestarian kuliner tradisional.

Multi Criteria Decision Making Method For Developing Smart Indonesia Program Scholarship Recipient Candidate System Supriyanta, Supriyanta; Sutanto, Yusuf; Susilo, Dahlan; Setyadi, Heribertus Ary; Syukron, Akhmad
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3706

The Government of Indonesia is continuously striving to improve its education quality with the provision of scholarship programs, one of which is the Smart Indonesia Program (SIP). Students' interest in obtaining SIPs is increasing, but the selection process still relies on conventional methods. Without adequate IT support, the selection process for SIP scholarship candidates will be complex, less objective, and somewhat unfair. State Vocational High School (SVHS) 5 Surakarta was selected as a case study for this research to establish the selection process and the data collection methods used in previous years. The research aims to develop a Decision Support System (DSS) to assist in nominating students deemed eligible for SIP scholarship recommendations. The applied methods include Analytical Hierarchy Process (AHP) and Multi-Objective Optimization by Ratio Analysis (MOORA). Four criteria have been set in this DSS: card ownership status, total parental income, household income, and number of siblings. Each of which is further broken down into several sub-criteria and assigned a value for use in the AHP process. Upon comparing data from 2021 to 2023, it was found that the accuracy in 2021 was 92.9%, in 2022 it reached 94.7%, and in 2023 it recorded 92.3%. Based on the results of this system accuracy test, it can be concluded that the AHP and MOORA methods can be used to objectively produce recommendations for students eligible for SIP scholarships, based on the input criteria.

Prediksi Harga Perumahan Menggunakan Metode Principal Component Analysis dan Random Forest Regresi Sutanto, Yusuf; Al Amin, Budi; Ary Setyadi, Heribertus; Eka Purnama, Bambang
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 6: Desember 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025126

Penentuan harga merupakan salah satu aspek krusial dalam kegiatan pengembangan properti mengingat hal tersebut akan mempengaruhi margin keuntungan yang diperoleh pengembang dan pilihan pembelian properti. Selama bertahun-tahun, prediksi harga rumah telah menjadi topik penelitian utama, karena permintaan rumah terus meroket. Sangat penting untuk mengembangkan kerangka kerja yang sesuai yang memungkinkan pembeli dan penjual untuk membuat keputusan cepat dalam hal membeli atau menjual properti. Dalam penelitian ini menggunakan metode Principal Component Analysis (PCA) dan Random Forest (RF), dengan tujuan untuk melakukan analisis akurasi penggunaan kedua metode dalam prediksi harga rumah dan untuk mengetahui pengaruh penggunaan PCA dalam mengoptimalkan metode random forest. Data yang digunakan adalah harga rumah di kota Surakarta berdasarkan hasil scraping data di situs propertygurugroup.com. Hasil analisis menunjukkan bahwa jumlah penjualan rumah tertinggi adalah daerah Plesungan, dan penjualan rumah yang memiliki sertifikat hak milik juga paling tinggi. Dari sepuluh variabel yang ada, luas tanah dan bangunan paling berpengaruh terhadap harga jual. Hasil pelatihan model menunjukkan bahwa peggabungan metode RF dan PCA memiliki nilai yang lebih optimal dibanding hanya menggunakan metode RF saja. Tingkat kesalahan dalam metode PCA lebih kecil, dengan rerata 0,0257 maka nilainya lebih konsisten dibanding hanya menggunakan metode RF yang nilai kesalahannya lebih besar dengan rerata 0,0332. Waktu pelatihan model menggunakan PCA lebih cepat (5005,75) dibanding hanya menggunakan metode RF (6099,25). Abstract Determining prices is one of the crucial aspects in property development activities considering that this will affect the profit margin obtained by developers and property purchase choices. Over the years, home price prediction has been a major research topic, as demand for homes continues to increase. It is important to develop a suitable framework that allows buyers and sellers to make quick decisions when it comes to buying or selling a property. This research uses the Principal Component Analysis (PCA) and Random Forest (RF) methods, with the aim of accuracy analyzing using both methods in predicting housing prices and to determine the effect of using PCA in optimizing the random forest method. The data used is house prices in Surakarta city based on data scraping results on propertygurugroup.com site. The analysis results show that the highest house sales is in Plesungan area, and houses sale with ownership certificates is also the highest. Of the ten variables, land area and building have the most influence on selling price. Model training results show that combination of RF and PCA methods has a more optimal value than using only RF method. Error rate in the PCA method is smaller, with an average of 0.0257, so the value is more consistent than just using the RF method, which has a larger error value with an average of 0.0332. The model training time using PCA is faster (5005.75) than just using the RF method (6099.25).

Capacity Building on Waste Management and TrashGo Application Utilization for the Community in Joglo, Surakarta Susanti, Nani Irma; Sutanto, Yusuf; Suseno, Adnan Terry; Setyadi, Heribertus Ary
DIKDIMAS : Jurnal Pengabdian Kepada Masyarakat Vol. 4 No. 3 (2025): DIKDIMAS : JURNAL PENGABDIAN KEPADA MASYARAKAT VOL 4 NO 3 DESEMBER 2025
Publisher : Asosiasi Profesi Multimedia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/dikdimas.v4i3.556

Background: The transaction mechanism for scrap items designates collectors as the purchasing party and community members or scavengers as the selling party. In operating their business, they still rely on manual methods, involving circulating door-to-door and relying on phone calls from regular customers. The current situation requires residents to wait for collectors to circulate their area and leaves them uninformed about the unpredictable and unstable pricing of scrap goods.Aims: This study aims to provide education provision regarding waste and scrap, followed by a workshop detailing the operation of the TrashGo application that was developed during prior activities.Methods: Asset-Based Community Development (ABCD) which consists of five stages: discovery, dream, design, define, and destiny.Result: Participants understood the business side of waste and scrap. Both collectors and the community were able to effectively utilize the TrashGo application for conducting scrap material transactions. A 95% level of understanding regarding the scrap business and application usage was achieved, signifying the training's success and utility.Conclusion: Data from questionnaires filled out by all participants show that this activity's targets were met. Based on the satisfaction survey, it can be concluded that participants were pleased and felt supported by the program.

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