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All Journal JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat
Mustika Fitri Larasati Sibuea
Universitas Royal
Computer Science & IT Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Social Sciences

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PEMANFAATAN SISTEM PENDUKUNG KEPUTUSAN DALAM PENGELOLAAN DISKON PAKAIAN DISTRO Muhammad Ardiansyah Sembiring; Mustika Fitri Larasati Sibuea; Rivaldi Rivaldi
Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat Vol. 4 No. 2 (2024): Desember 2024
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jpstm.v4i2.2762

Abstract

Abstract: Arif Distro is a business engaged in the sale of distro clothing. So far, what Arif Distro has done to make clothing prices discounted is still looking at how long the item has been sold. It is possible that there are still people interested in the item and there is no need to give a price cut or discount. Based on the problems that have been presented and the explanation, a decision support system is recommended that can help Arif Distro to choose which products to give distro clothing discounts to increase sales. The implementation of this community service utilizes a decision support system with the MOORA (MultiObjective Optimization by Ratio Analysis) method which is a decision-making model that uses a collective approach to its decision-making process. The criteria for the MOORA (MultiObjective Optimization by Ratio Analysis) method at Arif Distro include time (initial purchase), size, color, stock and type of clothing. The purpose of this activity is to be able to apply the MOORA (MultiObjective Optimization by Ratio Analysis) method in determining distro clothing discounts at Arif Distro and to be able to design a decision support system in determining distro clothing discounts at Arif Distro using the PHP programming language and MySQL database.Keywords: SPK; Discount; Distro Clothing Abstrak: Arif Distro merupakan suatu usaha yang bergerak dibidang penjualan pakaian distro. Selama ini yang dilakukan Arif Distro untuk membuat harga pakaian menjadi diskon masih melihat berapa lama barang tersebut laku terjual. Kemungkinan barang tersebut masih ada orang yang minat dan tidak perlu untuk diberikan potongan harga atau diskon. Berdasarkan permasalahan yang telah dipaparkan dan penjelasan tersebut, maka direkomendasikan sebuah sistem pendukung keputusan yang mampu membantu pihak Arif Distro untuk memillih produk mana yang hendak diberikan diskon pakaian distro untuk meningkatkan penjualan. Pelaksanaan pengabdian ini memanfaatkan sistem pendukung keputusan dengan metode MOORA (MultiObjective Optimization by Ratio Analysis) yang merupakan model pengambilan keputusan yang menggunakan pendekatan kolektif dari proses pengambilan keputusannya. Kriteria metode MOORA (MultiObjective Optimization by Ratio Analysis) pada Arif Distro diantaranya waktu (pembelian awal), size (ukuran), warna, stok dan jenis pakaian. Tujuan kegiatan ini adalah dapat menerapkan metode MOORA (MultiObjective Optimization by Ratio Analysis) dalam penentuan diskon pakaian distro di Arif Distro dan dapat merancang sistem pendukung keputusan dalam penentuan diskon pakaian distro di Arif Distro menggunakan bahasa pemrograman PHP dan database MySQL.Kata kunci: SPK; Diskon; Pakaian Distro
OPTIMASI MODEL KLASIFIKASI PESAN PENIPUAN DIGITAL BERBAHASA INDONESIA MENGGUNAKAN XGBOOST DENGAN BAYESIAN OPTIMIZATION DAN SMOTE Muhammad Ardiansyah Sembiring; Mustika Fitri Larasati Sibuea; Anjani Anjani
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 3 (2026): June 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i3.7212

Abstract

Abstract: Digital fraud through short messages has become a national problem in Indonesia; the Indonesia Anti-Scam Centre recorded 637,055 reports with losses of IDR 9.1 trillion up to July 2026. Scam messages are hard to filter with keywords because perpetrators keep changing wording and phone numbers, while official promotional messages share similar linguistic patterns. This study aims to optimize a three-class classifier (normal, fraud, promotion) for Indonesian messages by combining XGBoost, SMOTE, and Bayesian Optimization based on the Tree-structured Parzen Estimator (TPE), and to compare TPE with Grid Search and Random Search under an equal budget of 25 trials. Messages were normalized (URLs, phone numbers, amounts, and slang) and represented with combined word and character n-gram TF-IDF features. On 2,320 messages, XGBoost + SMOTE + TPE achieved a macro F1 of 0.9555, an accuracy of 96.34%, and a fraud-class recall of 0.9507, slightly higher than default XGBoost (F1 0.9531) and Grid Search (F1 0.9529), with a much lighter model that trained 3.2 times faster. Grid Search showed overfitting to the validation folds. The ablation study showed that SMOTE did not help under moderate class imbalance (F1 without SMOTE 0.9581), and Multinomial Naïve Bayes remained the best model (F1 0.9693). Most errors occurred between fraud and promotional messages. Keywords: Digital Fraud, Text Classification, XGBoost, Bayesian Optimization, SMOTE Abstrak: Penipuan digital melalui pesan singkat telah menjadi masalah nasional; Indonesia Anti-Scam Centre mencatat 637.055 laporan dengan kerugian Rp9,1 triliun hingga Juli 2026. Pesan penipuan sulit disaring dengan kata kunci karena pelaku terus mengubah variasi kata dan nomor telepon, sementara pesan promosi resmi memiliki pola bahasa yang mirip. Penelitian ini bertujuan mengoptimasi model klasifikasi tiga kelas (normal, penipuan, promo) untuk pesan berbahasa Indonesia dengan menggabungkan XGBoost, SMOTE, dan Bayesian Optimization berbasis Tree-structured Parzen Estimator (TPE), serta membandingkan TPE dengan Grid Search dan Random Search pada anggaran yang sama, yaitu 25 percobaan. Pesan dinormalisasi (tautan, nomor telepon, nominal, dan kata tidak baku) lalu direpresentasikan dengan fitur TF-IDF gabungan n-gram kata dan karakter. Pada 2.320 pesan, XGBoost + SMOTE + TPE mencapai F1-macro 0,9555, akurasi 96,34%, dan recall kelas penipuan 0,9507, sedikit lebih tinggi daripada XGBoost default (F1 0,9531) dan Grid Search (F1 0,9529), dengan model yang jauh lebih ringan dan waktu pelatihan 3,2 kali lebih cepat. Grid Search menunjukkan gejala overfitting terhadap fold validasi. Uji ablasi menunjukkan SMOTE tidak membantu pada ketidakseimbangan kelas moderat (F1 tanpa SMOTE 0,9581), dan Multinomial Naïve Bayes tetap menjadi model terbaik (F1 0,9693). Sebagian besar kesalahan terjadi antara pesan penipuan dan promo. Kata Kunci: Penipuan Digital, Klasifikasi Teks, XGBoost, Bayesian Optimization, SMOTE.
PENGARUH MODEL PROBLEM BASED LEARNING BERPENDEKATAN PEMBELAJARAN MENDALAM BERBANTUAN AI GENERATIF TERHADAP KEMAMPUAN BERPIKIR KOMPUTASIONAL DAN NUMERASI SISWA SMP Mustika Fitri Larasati Sibuea; Muhammad Ardiansyah Sembiring; Yudi Firdiansyah Putra Sitorus
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 3 (2026): June 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i3.7213

Abstract

Abstract: Indonesian students' mathematics achievement remains low: the 2026 national academic ability test (TKA) for junior high schools recorded an average mathematics score of 40.34, and Indonesia scored 364 in PISA 2025 mathematics, far below the OECD average of 463. Meanwhile, the government has introduced the Deep Learning approach and the Coding and Artificial Intelligence subject, which require computational thinking, while unrestricted generative AI has been shown to harm mathematics learning. This study examined the effect of Problem Based Learning with a Deep Learning approach assisted by guardrailed generative AI (PBL-DL + AI) on the computational thinking and numeracy of junior high school students. A quasi-experimental nonequivalent pretest-posttest control group design involved 95 eighth-grade students in three groups: PBL-DL + AI, PBL-DL without AI, and conventional instruction, on systems of linear equations in two variables. Data were analyzed using MANCOVA, ANCOVA, and Bonferroni post hoc tests. The results showed a significant multivariate effect (Wilks' λ = 0.752, F = 6.81, p < .001, ηp² = 0.13). For computational thinking, only the PBL-DL + AI group significantly outperformed the conventional group (d = 1.02), with the largest gains in abstraction and algorithm indicators. For numeracy, both PBL-DL groups outperformed the conventional group, with no additional benefit from AI. Guardrailed AI should therefore be positioned as a tutor that scaffolds thinking, not a replacement for meaningful contextual problems. Keywords: Problem Based Learning, Deep Learning Approach, Generative AI, Computational Thinking, Numeracy   Abstrak: Capaian matematika siswa Indonesia masih rendah: rata-rata nilai matematika Tes Kemampuan Akademik (TKA) SMP 2026 hanya 40,34, dan skor matematika PISA 2025 Indonesia sebesar 364, jauh di bawah rata-rata OECD (463). Di sisi lain, pemerintah menerapkan pendekatan Pembelajaran Mendalam dan mata pelajaran Koding dan Kecerdasan Artifisial yang menuntut kemampuan berpikir komputasional, sementara penggunaan AI generatif tanpa pembatas terbukti dapat merugikan pembelajaran matematika. Penelitian ini bertujuan menganalisis pengaruh model Problem Based Learning berpendekatan Pembelajaran Mendalam berbantuan AI generatif ber-guardrail (PBL-PM + AI) terhadap kemampuan berpikir komputasional dan numerasi siswa SMP. Penelitian kuasi-eksperimen dengan desain nonequivalent pretest-posttest control group melibatkan 95 siswa kelas VIII dalam tiga kelompok, yaitu PBL-PM + AI, PBL-PM tanpa AI, dan pembelajaran konvensional, pada materi sistem persamaan linear dua variabel. Data dianalisis dengan MANCOVA, ANCOVA, dan uji lanjut Bonferroni. Hasil menunjukkan pengaruh multivariat yang signifikan (Wilks' λ = 0,752; F = 6,81; p < 0,001; η²p = 0,13). Pada berpikir komputasional, hanya kelompok PBL-PM + AI yang unggul signifikan atas kelompok konvensional (d = 1,02), dengan peningkatan terbesar pada indikator abstraksi dan algoritma. Pada numerasi, kedua kelompok PBL-PM unggul atas kelompok konvensional tanpa tambahan manfaat dari AI. AI ber-guardrail sebaiknya diposisikan sebagai tutor yang membimbing proses berpikir, bukan pengganti masalah kontekstual yang bermakna. Kata Kunci: Problem Based Learning, Pembelajaran Mendalam, AI Generatif, Berpikir Komputasional, Numerasi.
Co-Authors Anjani Anjani Bima Aditya Diana Selvi Febby Madonna Yuma Fitrah Adinda Gina Safira Hokkop Efendi Hasibuan Indra Ramadona Harahap Masitah Handayani Muhammad Ardiansyah Sembiring Muhammad Iqbal Nadira Meutia Arifin Novita elfina Parini Parini Raja Tama Andri Agus Riki Andri Yusda Rina Julita Rivaldi Rivaldi sahren, sahren Salwa Yuma Annisa Sundari Sundari Yudi Firdiansyah Putra Sitorus