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All Journal Jupiter Jurnal Pendidikan Matematika RAFA Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal INTECH (Informatika dan Teknologi)
Imelda Saluza
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Social Sciences

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PENGARUH PEMBELAJARAN GEOMETRI ANALITIK MENGGUNAKAN PENDEKATAN PAIKEM Imelda Saluza
Jurnal Pendidikan Matematika RAFA Vol 1 No 1 (2015): JURNAL PENDIDIKAN MATEMATIKA RAFA
Publisher : Program Studi Pendidikan Matematika UIN Raden Fatah Palembang

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

Abstract

Geometri analitik adalah suatu cabang ilmu matematika yang merupakan penggabungan antara aljabar dan geometri. Hal ini berarti untuk dapat memahami aljabar dapat menggunakan gemotri ataupun sebaliknya. Berdasarkan pengamatan peneliti terhadap mahasiswa yang mengambil mata kuliah geometri analitik, mahasiswa sering mengalami kesulitan untuk memahami materi. Penyebab utamanya adalah mahasiswa kurang antusias mengikuti pembelajaran, daya kreativitasnya rendah, dan bersikap acuh tak acuh sedangkan materi geometri analitik mentut keaktifan siswa untuk lebih memahami konsep aljabar secara geometrik. Hal ini menunjukkan bahwa proses pembelajaran secara konvensional yang biasa dilakukan tidak mampu mendorong mahasiswa untuk menggunakan daya pikirnya secara optimal, akibatnya mahasiswa menjadi kurang aktif dan proses pembelajaran menjadi kurang efektif. Untuk meningkatkan kemampuan mahasiswa dalam mengaitkan konsep-konsep aljabar menggunakan geometrik, maka dalam penelitian ini dilakukan pembelajaran dengan menggunakan pendekatan PAIKEM
PENENTUAN TINGKAT KEKUMUHAN PERMUKIMAN KUMUH KOTA PALEMBANG DENGAN METODE ALGORITMA K-MEANS CLUSTERING DAN ALGORITMA ID3 Lastri Widya Astuti; Endah Puspita Sari; Imelda Saluza; Faradillah Faradillah; Rini Yunita
INTECH Vol 2 No 1 (2021): INTECH (Informatika Dan Teknologi)
Publisher : Program Studi Informatika Fakultas Teknik dan Komputer Universitas Baturaja

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (584.102 KB) | DOI: 10.54895/intech.v2i1.869

Abstract

Slum settlers are a condition of uninhabitable settlements. Slums are devided into 4 levels, namely : high slums, medium, light and not slum. To produce these four levels, method is used namely K-Means Clustering Algorithm and the ID3 Algorithm is used to give priority with pre determined attributes then accumulated with the results of clustering classified as slum level. The accuracy test is performe by using the confusion matrix method, where the data results from the K-Means Clustering method compared to the baseline data. The results obtained from the accuracy with confusion matrix is 0,70%, which means the level of truth (accuracy) between the result of the baseline data with research data is 70%.
Ensemble Backpropagation Neural Network Dalam Memprediksi Inflasi Imelda Saluza; Lastri Widya Asuti; Dhamayanti; Evi Yulianti
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 15 No 1d (2023): Jupiter Edisi April 2023
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281./6613/15.jupiter.2023.04

Abstract

Global economic volatility that continues to experience spikes is a particular concern for countries in the world, including Indonesia. This is due to the impact that will occur if it continues to increase which can result in a country's economic recession. A country must pay attention to the pressure on the inflation rate. Unreasonable inflation rate volatility can have a negative impact on economic growth. Therefore, it is very important to accurately predict future inflation rates so that it becomes important information for economic policy makers. Inflation prediction is one of the problems that has been widely researched because the data is non-stationary and non-linear, so an algorithm is needed that can overcome this problem. One of the algorithms that can be used is the Backpropagation Neural Network (BPNN), but the BPNN network in its application has many parameters that must be determined so that it often causes overfitting. For this reason, instead of learning from multiple models, the ensemble method is used. The main benefit of this method is to reduce overfitting and at the same time maintain the accuracy and diversity of the BPNN network.
Penentuan Tingkat Kekumuhan Permukiman Kumuh Kota Palembang Dengan Metode Algoritma K-Means Clustering Dan Algoritma ID3 Endah Puspita Sari; Lastri Widya Astuti; Imelda Saluza; Faradillah Faradillah; Rini Yunita
INTECH (Informatika dan Teknologi) Vol 2 No 1 (2021): INTECH (Informatika Dan Teknologi)
Publisher : Informatics Study Program, Faculty of Engineering and Computers, Baturaja University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54895/intech.v2i1.869

Abstract

Slum settlers are a condition of uninhabitable settlements. Slums are devided into 4 levels, namely : high slums, medium, light and not slum. To produce these four levels, method is used namely K-Means Clustering Algorithm and the ID3 Algorithm is used to give priority with pre determined attributes then accumulated with the results of clustering classified as slum level. The accuracy test is performe by using the confusion matrix method, where the data results from the K-Means Clustering method compared to the baseline data. The results obtained from the accuracy with confusion matrix is 0,70%, which means the level of truth (accuracy) between the result of the baseline data with research data is 70%.
Penguatan Kompetensi Guru SMK PGRI Kota Palembang Melalui Pemanfaatan Artificial Intelligence Dalam Perencanaan Pembelajaran Ahmad Sanmorino; Hendra Di Kesuma; Indah Pratiwi Putri; Lastri Widya Astuti; Imelda Saluza; Tasmi; Nining Ariati; Dhamayanti; Faradillah; Fery Antony; Dona Marcelina; Rudi Heriansyah
Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Vol. 9 No. 1 (2026): Januari 2026
Publisher : STMIK Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurdimas.v9i1.4131

Abstract

Abstract: The development of artificial intelligence (AI) technology presents new opportunities in education, particularly in lesson planning. However, most vocational high school teachers in Palembang City, including those at SMK PGRI 2, still have limited knowledge and skills in utilizing AI. This problem is the background to the implementation of community service activities (PkM) with the aim of improving teacher competency in using Large Language Models (LLM) such as Gemini and ChatGPT to develop Lesson Implementation Plans (RPP). The methods used included needs surveys, interactive workshops, hands-on practice, and evaluation through post-tests and participant feedback. The results of the activity showed a significant increase, where teacher knowledge increased from 20% to 80% and the application of AI in lesson plans increased from 10% to 65%. The contribution of this activity lies in improving teachers' ability to utilize AI to develop lesson plans more effectively and providing a scientific basis for the application of LLM in lesson planning in vocational education. Keywords: artificial intelligence, lesson planning, vocational school teachers Abstrak: Perkembangan teknologi kecerdasan buatan (Artificial Intelligence) menghadirkan peluang baru dalam dunia pendidikan, khususnya dalam perencanaan pembelajaran. Namun, sebagian besar guru SMK di Kota Palembang, termasuk di SMK PGRI 2, masih memiliki keterbatasan dalam pengetahuan dan keterampilan pemanfaatan AI. Permasalahan ini melatarbelakangi dilaksanakannya kegiatan pengabdian kepada masyarakat (PkM) dengan tujuan meningkatkan kompetensi guru dalam menggunakan Large Language Models (LLM) seperti Gemini dan ChatGPT untuk menyusun Rencana Pelaksanaan Pembelajaran (RPP). Metode yang digunakan meliputi survei kebutuhan, workshop interaktif, praktik langsung, serta evaluasi melalui post-test dan umpan balik peserta. Hasil kegiatan menunjukkan adanya peningkatan signifikan, di mana pengetahuan guru meningkat dari 20% menjadi 80% dan penerapan AI dalam RPP naik dari 10% menjadi 65%. Kontribusi kegiatan ini terletak pada peningkatan kemampuan guru dalam memanfaatkan AI untuk menyusun RPP secara lebih efektif serta penyediaan dasar ilmiah bagi penerapan LLM dalam perencanaan pembelajaran di pendidikan vokasi. Kata kunci: artificial intelligence, guru SMK, perencanaan pembelajaran
Co-Authors Ahmad Sanmorino Dhamayanti Dhamayanti Dona Marcelina Endah Puspita Sari Faradillah Faradillah Faradillah Fery Antony Hendra Di Kesuma Indah Pratiwi Putri Lastri Widya Astuti Lastri Widya Asuti nining ariati Nurwijayanti Rini Yunita Rudi Heriansyah Tasmi Yulianti, Evi