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Politeknik Manufaktur Negeri Bangka Belitung
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Pengembangan Aplikasi Point Of Sale Berbasis Android Pada UMKM Nyemil Beauty Menggunakan Metode Agile Laras Niti Mulyani; Muhammad Iqbal; Adinda Islamaya; Rati; Fionalita Rachmadianti
Jurnal Sains Informatika Terapan Vol. 4 No. 3 (2025): Jurnal Sains Informatika Terapan (Oktober, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v4i3.837

Abstract

Nyemil Beauty, UMKM kuliner kontemporer, menghadapi tantangan serius karena pencatatan transaksi manual yang tidak efisien, memperlambat operasional, rentan kesalahan, dan risiko kehilangan data inventaris. Urgensi ini mendorong studi untuk merancang dan mengembangkan aplikasi Point of Sale (POS) berbasis Android.Aplikasi ini bertujuan mengotomatisasi pengelolaan transaksi, data produk, dan laporan penjualan UMKM. Pengembangan menggunakan metode Agile, dengan perancangan meliputi analisis kebutuhan, penyusunan diagram, dan implementasi menggunakan Android Studio, bahasa Kotlin, serta basis data SQLite.Fitur utama aplikasi mencakup pencatatan produk dan transaksi, serta pembuatan laporan penjualan digital. Hasil pengujian pengguna menunjukkan penerimaan positif: 41,87% sangat baik, 55,25% baik, dan 2,5% cukup. Aplikasi POS berbasis Android ini berhasil meningkatkan efisiensi dan akurasi transaksi penjualan di Nyemil Beauty, terbukti mudah digunakan, andal, dan menyediakan solusi permanen atas masalah pencatatan manual.
Deteksi Emosi pada Teks Media Sosial Menggunakan Bidirectional LSTM dan Glove Word Embedding Weri Sirait; Rahmat Hidayat; Laras Niti Mulyani; Arika Juwita Z; Nurhidayat
Technologica Vol. 5 No. 2 (2026): Technologica
Publisher : Green Engineering Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55043/technologica.v5i2.630

Abstract

Setiap harinya, platform media sosial memproduksi jutaan teks yang memuat beragam ekspresi emosional penggunanya. Kemampuan untuk mengenali emosi secara otomatis memiliki manfaat strategis dalam berbagai bidang, seperti analisis sentimen, pemantauan citra merek, dan pengawasan kesehatan mental. Studi ini mengajukan model deteksi emosi yang dibangun berbasis Bidirectional Long Short-Term Memory (BiLSTM) dan dipadukan dengan Glove Word Embedding berdimensi 100 (dilatih pada 6 miliar token dengan kosakata 400.000 kata dari Common Crawl) guna mengklasifikasikan teks media sosial ke dalam enam kategori emosi: joy, sadness, anger, fear, love, dan surprise. Data yang digunakan berasal dari Emotions Dataset for NLP yang tersedia di Kaggle, mencakup 20.000 sampel teks berbahasa Inggris dengan sebaran kelas yang tidak merata (joy: 5.362, sadness: 4.666, anger: 2.159, fear: 1.937, love: 1.304, surprise: 572 pada data latih). Keunggulan penelitian ini terletak pada kajian komparatif yang terstruktur, yang menunjukkan bahwa kualitas representasi vektor melalui pretrained Glove memberikan dampak lebih besar dibandingkan sekadar peningkatan arsitektur (BiLSTM tanpa pretrained), serta bahwa penggabungan keduanya secara sinergis mampu melampaui model baseline. Model BiLSTM + Glove yang diusulkan mencapai akurasi 89,35%, presisi macro 88,12%, recall macro 87,64%, dan F1-score macro 87,88% pada data uji, melampaui LSTM + Glove (86,10%) dan BiLSTM tanpa pretrained embedding (84,75%). Selisih performa ini diperkuat oleh analisis kurva konvergensi dan confusion matrix yang memperlihatkan keunggulan yang konsisten di seluruh kelas emosi
Characterization of Cassava Sludge and Tofu Wastewater as Anaerobic Digestion Substrates for Biogas Production Daya Wulandari; Riztamala Diana; Laras Niti Mulyani; Julien Alpa Harzon; Mella Aulia
International Journal of Research in Vocational Studies (IJRVOCAS) Vol. 6 No. 2 (2026): IJRVOCAS - August
Publisher : Yayasan Ghalih Pelopor Pendidikan (Ghalih Foundation)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53893/ijrvocas.v6i2.528

Abstract

The increasing generation of organic waste from the food processing industry has created opportunities for converting biodegradable residues into renewable energy. Cassava sludge and tofu wastewater contain biodegradable organic matter and may serve as substrates for anaerobic digestion. This study aimed to characterize the initial physicochemical properties of cassava sludge and tofu wastewater and assess their characteristics and endpoint gas profiles under different substrate formulations. Four formulations were evaluated: cassava sludge (S1), cassava sludge mixed with tofu wastewater (S2), tofu wastewater (S3), and cassava sludge supplemented with a microbial inoculum (S4). Activated Effective Microorganisms 4 (EM4) was applied to all formulations. Initial characterization included pH, temperature, total nitrogen (TN), total suspended solids (TSS), turbidity, chemical oxygen demand (COD), and biochemical oxygen demand (BOD). Reactor pH and temperature were monitored every three days for 21 days batch digestion period, followed by endpoint gas composition analysis. Cassava sludge exhibited the highest initial COD and BOD concentrations, at 19,409 and 8,640 mg/L, respectively, with TSS and TN concentrations of 680 and 270 mg/L. The cassava sludge tofu wastewater formulation showed COD and BOD concentrations of (13,355 mg/L and 6,028 mg/L), respectively, whereas tofu wastewater showed values of (6,644 mg/L and 2,974 mg/L). S4 exhibited lower initial COD and BOD concentrations of 389 and 175 mg/L, respectively, reflecting the initial characteristics of the formulation containing additional microbial inoculum. During the digestion period, temperatures ranged from (25.1–32.2 °C). while pH generally increased across the formulations. At the end of the 21 days digestion period, S4 showed the highest CH₄ reading (65.4%LEL), followed by S1 (9.7%LEL), S3 (4.7%LEL), and S2 (3.9%LEL). The observed results indicate differences in endpoint gas characteristics among the substrate formulations under the experimental conditions. Overall, the initial physicochemical characteristics and endpoint gas profiles provide a basis for assessing cassava sludge and tofu wastewater as substrates for anaerobic digestion.
Co-Authors Adinda Islamaya Arika Juwita Z Daya Wulandari Fionalita Rachmadianti Julien Alpa Harzon Mella Aulia Muhammad Iqbal Nurhidayat Rahmat Hidayat Rati Riztamala Diana Weri Sirait