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All Journal Jurnal Ilmu Kesehatan Indonesia (JIKSI) Jurnal Riset Indragiri INTECH (Informatika dan Teknologi)
Alamsyah, Lucky
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Religion Agriculture, Biological Sciences & Forestry Humanities Chemistry Computer Science & IT Economics, Econometrics & Finance Education Engineering Health Professions Immunology & microbiology Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Public Health

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ANALISIS USAHA DAN NILAI TAMBAH GULA SEMUT DI DESA RUMBAI JAYA KECAMATAN KEMPAS KABUPATEN INDRAGIRI HILIR ( Studi Kasus Usaha Gula Semut Gendis Makmur ) Alamsyah, Lucky; Yuslizar
JURNAL RISET INDRAGIRI Vol 2 No 3 (2023): November
Publisher : Lembaga Marwah Rakyat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61069/juri.v2i3.72

Abstract

Gula semut adalah gula kelapa berbentuk bubuk yang dibuat dari nira kelapa yang telah dilebur kembali dengan penambahan air pada konsentrasi tertentu. Industri Gendis Makmur merupakan salah satu industri pengolahan gula semut berskala rumah tangga. Tujuan penelitian ini adalah untuk mengetahui biaya produksi, pendapatan, keuntungan, efisiensi dan nilai tambah pada industri gula semut Gendis Makmur. Analisis data yang digunakan adalah analisis usaha dan analisis nilai tambah. Hasil penelitian menunjukkan bahwa (1) Penerimaan yang diperoleh sebesar Rp 1.750.000 per produksi (2) Total biaya produksi yang dikeluarkan sebesar Rp 1.017.596 per produksi (3) Keuntungan yang diperoleh sebesar Rp 732.404 per produksi (4) Nilai tambah yang diperoleh sebesar Rp 9.780/kg.
Korelasi Kadar HbA1c dengan Kadar D-dimer pada Pasien Diabetes Melitus Tipe 2 Alamsyah, Lucky; Yulia, Dwi; Aprilia, Dinda
Jurnal Ilmu Kesehatan Indonesia Vol. 5 No. 2 (2024): Juni 2024
Publisher : Fakultas Kedokteran, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jikesi.v5i2.1079

Abstract

Abstrak Latar Belakang: Diabetes melitus tipe 2 (DM tipe 2) adalah penyakit gangguan metabolik yang ditandai dengan peningkatan gula darah akibat penurunan sekresi insulin oleh sel beta pankreas atau ganguan fungsi insulin (resistensi insulin) atau kedua-duanya. Peningkatan kadar HbA1c dan peningkatan kadar D-dimer merupakan dua kondisi pemeriksan hematologi yang bisa ditemukan dalam perjalanan penyakit DM tipe 2. Objektif: Penelitian ini bertujuan untuk mengetahui korelasi kadar Hba1c dengan kadar D-dimer pada pasien diabetes melitus tipe 2 . Metode: Penelitian ini merupakan analitik dengan pendekatan cross sectional pada data sekunder pasien diabetes melitus tipe 2 yang dilakukan pemeriksaan kadar HbA1c dan kadar D-dimer dengan alat POCT. Sampel penelitian ini adalah pasien yang didiagnosis DM tipe 2 dengan kadar HbA1c terkontrol (≤7 %) di Rumah Sakit Umum (RSU) Prof. Dr. M Ali Hanafiah Batu Sangkar, Kabupaten Tanah Datar sebanyak 20 sampel. Waktu penelitian periode November 2020–April 2021. Hasil: Penelitian diperoleh nilai rerata untuk kadar HbA1c pasien DM tipe 2 sebesar 5,925% dan kadar D-dimer sebesar 432 ng/mL. Analisis statistik uji korelasi Pearson menunjukkan tidak terdapat korelasi yang bermakna antara kadar HbA1c dengan kadar D-dimer (r=0,081; p=0,257). Kesimpulan: Pada penelitian ini dapat disimpulkan tidak ditemukan korelasi antara kadar HbA1c dengan kadar D-dimer pada diabetes melitus tipe 2. Kata kunci: D-dimer, Diabetes Melitus Tipe 2, HbA1c Abstract Background: Type 2 Diabetes is a metabolic disorder characterized by increased blood sugar due to decreased insulin secretion by pancreatic beta cells or insulin function disorders (insulin resistance) or both. Increased levels of HbA1c and increased levels of D-dimer are two conditions for hematological examination that can be found in the course of Type 2 diabetes. Objective: This study aims to determine whether there was a correlation between HbA1c levels and D-dimer levels in patients with Type 2 diabetes. Methods: This study was an analytic study with a cross sectional approach to secondary data for patients with type 2. 2 diabetes who were examined for HbA1c levels and D-dimer levels using POCT. The sample of this study were patients diagnosed with type 2 diabetes with controlled HbA1c levels (≤7 %) at Prof. Dr. M Ali Hanafiah Batu Sangkar, Tanah Datar Regency as many as 20 samples. The research period was November 2020 - April 2021. Results: Data analysis used univariate and bivariate analysis with the Pearson correlation test. Correlation was significant if p <0.05. The study obtained that the mean value for the HbA1c level of Type 2 diabetes patients was 5.925% and the D-dimer level was 432 ug / dL. The results of the bivariate analysis showed the relationship between HbA1c levels and D-dimer levels, the p value was 0.734 (> 0.05) and the correlation value (r) was 0.081. Conclusion: So it can be concluded that there is a weak correlation between HbA1c levels and D-dimer levels in patients with Type 2 diabetes.
Systematic Literature Review: Bankruptcy Prediction Menggunakan Teknik Machine Learning dan Deep Learning Sulistiani, Ita; Mufida, Erdiana; Yasser, Pekik M.; Alamsyah, Lucky
INTECH 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

Abstract

Bankruptcy prediction has long been an interesting topic to be discussed and developed by many researchers. Initially, the development of bankruptcy prediction models was carried out through statistical methods by examining corporate's financial ratios. Along with the development of technology, the use of machine learning and deep learning techniques for developing bankruptcy prediction models has begun to be widely used, for example, SVM, ANN, hybrid genetic algorithm, fuzzy-SVM, convolutional neural network, textual disclosure, and so on. Although machine learning and deep learning models have various advantages over statistical methods, they still have many drawbacks. In this paper, we will review machine learning and deep learning models used in bankruptcy prediction using the Systematic Literature Review method. Based on our research, bankruptcy prediction models developed using machine learning or deep learning techniques can outperform the performance of models using classical statistical methods.
Co-Authors Aprilia, Dinda Mufida, Erdiana Sulistiani, Ita Yasser, Pekik M. Yulia, Dwi Yuslizar