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All Journal InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Jurnal Ilmiah FIFO JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Riset Informatika Jurnal Informatika Kaputama (JIK) Infotek : Jurnal Informatika dan Teknologi Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Abdimas Indonesia : Jurnal Abdimas Indonesia Abditeknika - Jurnal Pengabdian Kepada Masyarakat Wasana Nyata IMTechno: Journal of Industrial Management and Technology Paradigma Indonesian Journal Computer Science (ijcs) WASANA NYATA Jurnal Infortech
Wawan Nugroho
Universitas Bina Sarana Informatika
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Veterinary Other

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Implementation MFEP Method in Developing Recommendation System for Program Keluarga Harapan (PKH) Recipients Wawan Nugroho; Galih Setiawan Nurohim; Heribertus Ary Setyadi Setyadi; Doddy Satrya Perbawa
Paradigma - Jurnal Komputer dan Informatika Vol. 26 No. 2 (2024): September 2024 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v26i2.4978

Abstract

Poverty occurs because of the imbalance between unlimited human needs and limited resources. This results in a lack of income to meet basic living needs. The Indoonesian government's efforts to alleviate poverty include providing assistance to the poor or underprivileged with assistance called Social Assistance, one of which is the Program Keluarga Harapan (PKH). Problems often occur in determining who is entitled to receive PKH assistance. The conventional selection process is considered inefficient because it requires a long process and the influence of the committee's subjectivity in the assessment, the criteria used in the survey are not in accordance with government regulations and the limited quota of total PKH recipients, so there are still people who do not receive PKH even though they meet the criteria. This research uses the Multi Factor Evaluation Process (MFEP) method. System testing uses the black box method and Boundary Value Analysis techniques which focus on finding system errors. To test the system's accuracy by comparing the MFEP process from the system results and facts based on PKH recipients in 2022 and producing an accuracy value of 91%.
COMPARATION OF DECISION TREE MODEL AND SUPPORT VERCTOR MACHINE IN SENTIMENT ANALYSIS OF REVIEW DATASET SAMSUNG SSD 850 EVO AT NEW EGG SHOP Muhammad Fahmi Julianto; Yesni Malau; Wahyutama Fitri Hidayat; Wawan Nugroho; Fintri Indriyani
Jurnal Riset Informatika Vol. 3 No. 4 (2021): September 2021 Edition
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v3i4.97

Abstract

The development of information technology is currently growing very rapidly, including the impact on the hardware used. This can be exemplified in the use of hard drives that are starting to switch to SSDs. The process of selecting an SSD product to be used cannot be separated from the sources of information found on the internet. Through the internet, every user can provide reviews, both positive and negative reviews. With the many reviews regarding the review of the Samsung 850 Evo SSD on the NewEgg Store, the author uses it to be processed into information, which will have new knowledge. Based on that, the author makes research, in the form of opinion classification by analyzing sentiment through a text mining approach. In this study, two classification models were used, namely Decision Tree and Support Vector Machine. The results of this study are in the form of a comparison of the 2 models used based on the accuracy and AUC values. Based on research, the Support Vector Machine model is better than the Decision Tree model. This conclusion can be proven by the accuracy value of the Support Vector Machine model resulting in a value of 0.87 or 87% while the accuracy value of the Decision Tree model produces a value of 0.82 or 82%. In addition, the AUC value of the Support Vector Machine model produces a value of 0.87 and the Decision Tree mode produces a value of 0.82 or it can be said that the AUC value of the Support Vector Machine model is better than the Decision Tree model.
Scratch Sebagai Media Stimulasi Kognitif: Penguatan Berpikir Komputasional Berbasis Service Learning pada Pendidikan Dasar Heribertus Ary Setyadi; Wawan Nugroho; Supriyanta Supriyanta; Candra Agustina
WASANA NYATA Vol 10, No 1 (2026)
Publisher : STIE AUB Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36587/wasananyata.v10i1.2199

Abstract

Wacana integrasi kelas pemrograman bagi siswa dasar dan menengah yang diusulkan oleh Wakil Presiden Gibran Rakabuming Raka memicu restrukturisasi kurikulum nasional oleh Kementerian Pendidikan Dasar dan Menengah melalui penyusunan kerangka strategis berbasis coding dan Kecerdasan Buatan (AI). Edukasi pemrograman pada usia dini diproyeksikan mampu menstimulasi penalaran kreatif serta mengonstruksi soft skills abad ke-21 termasuk problem solving, kolaborasi, dan critical thinking, sehingga orientasi siswa bergeser dari konsumen instruksi menjadi arsitek solusi digital. Selaras dengan arah kebijakan tersebut, artikel/kegiatan ini mengkaji program kemitraan bersama Pondok Pesantren dan Panti Asuhan Al Ikhsan Surakarta dalam memitigasi urgensi kesenjangan digital (digital divide) dan keterbatasan logika sistematis siswa. Melalui intervensi teknologis yang terarah, program ini mengombinasikan instruksi teknis dan penguatan psikologis guna memperluas domain kognitif serta membangun efikasi diri siswa. Hasil implementasi menegaskan bahwa sintesis antara metode Service Learning dan platform Scratch efektif mengakselerasi literasi digital serta pertumbuhan kognitif. Transformasi peserta dari pengguna pasif menjadi kreator aktif diindikasikan oleh penguasaan empat pilar Berpikir Komputasional (Computational Thinking): dekomposisi, pengenalan pola, abstraksi, dan perancangan algoritma, yang secara simultan meningkatkan kapabilitas pemecahan masalah secara signifikan.
Co-Authors ., Supriyanta Akhmad Syukron Al Amin, Budi Annida Purnamawati Aulia, Feryoda Ashafiro Candra Agustina Dahlan Susilo Dehant Mahendra H Destiana Putri Doddy Satrya Perbawa Fauzi Ahmad Muda Fintri Indriyani Galih Setiawan Nurohim Galih Setyawan Nurohim Hadi, Ahmad Aliyul Hadi, Eva Kristiana Hani Yulia Rachma Heribertus Ary Setyadi Heribertus Ary Setyadi Setyadi Muhammad Fahmi Julianto Nugroho, Andika Satrio Ramadhan, Tegar Andriyan Rozaaq, Fattahillah Wahyu Nur Ruswanti, Diyah Sardiarinto Sardiarinto Satrya Perbawa, Doddy Setiaji Setiaji, Setiaji Sihombing, Hamonangan Ardiansyah Sola Gracia Deo Andrew Supriyanta Supriyanta Supriyanta Supriyanta Supriyanta Susilowati, Yuni Sutanto Sutanto Sutanto Sutanto Wahyutama Fitri Hidayat Wiranto , Roni Yesni Malau Yusuf Sutanto