Judul: Detecting Suicidal Ideation of Tweets Using Long Short-Term Memory and Fasttext Word Embedding
Abstrak: Suicide is a global health crisis. It has become the second greatest cause of mortality for people aged 15 to 29. In Indonesia, around five people commit suicide every day. To avert the tragedy of suicide in diverse high-risk groups, early detection of suicidal behaviors and adequate and timely interventions are essential. The younger generation has started to turn to the Internet to seek help and discuss depression and suicide-related topics. The huge amount of textual data generated by users on SNS became the main component in building the early detection tool. For a binary text classification task, LSTM Performs better compared to CNN. Fasttext word embedding can handle uncommon words, misspelled words, and word suffixes and prefixes. This research aims to build an accurate text classification model using LSTM and fasttext to identify tweets containing suicidal ideation. Without applying any text preprocessing or imbalanced class treatments, the model had outstanding performance with a 78% sensitivity, 97% specificity, and an 88% F1 score. No text preprocessing technique led to an improvement in the F1 score. However, improvements in sensitivity were achieved through the implementation of class weighting and oversampling. Notably, the ADASYN technique yielded a substantial sensitivity increase., Bunuh diri merupakan krisis kesehatan global. Bunuh diri telah menjadi penyebab kematian terbesar kedua bagi kelompok umur 15 hingga 29 tahun. Di Indonesia, ada sekitar lima korban bunuh diri setiap hari. Untuk mencegah tragedi bunuh diri dalam berbagai kelompok berisiko tinggi, deteksi dini terhadap perilaku bunuh diri dan intervensi yang tepat dan cekatan menjadi sangat penting. Generasi muda beralih ke Internet untuk mencari bantuan dan mendiskusikan topik terkait depresi dan bunuh diri. Besarnya jumlah data teks yang dihasilkan oleh pengguna di jejaring sosial menjadi komponen utama dalam membangun alat deteksi dini. LSTM memiliki performa lebih baik dibandingkan dengan CNN dalam melakukan klasifikasi teks. Fasttext dapat menangani kata-kata yang tidak umum digunakan, kata-kata yang salah eja, serta kata berimbuhan dengan baik. Penelitian ini bertujuan untuk membangun model klasifikasi teks menggunakan LSTM dan fasttext untuk mengidentifikasi tweet yang mengandung pemikiran bunuh diri. Tanpa menerapkan metode pra-pemrosesan teks dan penanganan kelas yang tidak seimbang, model telah memiliki performa yang baik dengan sensitivitas 78%, spesifisitas 97%, dan skor F1 88%. Seluruh teknik pra-pemrosesan teks yang digunakan dalam penelitian ini tidak dapat meningkatkan skor F1. Namun, peningkatan sensitivitas tercapai melalui implementasi pembobotan kelas dan oversampling. Terutama, teknik ADASYN menghasilkan peningkatan sensitivitas yang signifikan.
Keyword: fasttext, LSTM, suicidal ideation, text classification, twitter

Judul: Suicidal Tweet Classification Using Random Forest
Abstrak: Bunuh diri adalah salah satu penyebab kematian tertinggi secara global. Data Twitter dapat digunakan dalam pengidentifikasian pengguna internet yang berkecenderungan bunuh diri. Random forest merupakan salah satu metode yang populer digunakan untuk data berdimensi tinggi dan pada analisis data teks. Penelitian ini bertujuan untuk menggunakan metode random forest untuk memprediksi tweet berkecenderungan bunuh diri dan mengidentifikasi kata-kata yang dianggap penting dalam klasifikasi tweet yang berkecenderungan bunuh diri. Data sebanyak 10,006 tweets memiliki proporsi label 30:70. Penelitian menerapkan model random forest dengan menguji hyperparameter ntree dan mtry dengan 10- fold cross validation. Penelitian ini menerapkan oversampling dan undersampling. Model random forest dengan oversampling dan ntree 50 dan mtry 176 dapat mengklasifikasikan tweet berkecenderungan bunuh diri dengan sensitivitas 0.594 dan f1-score 0.649. Berdasarkan mean decrease accuracy, kata ‘nyerah’ memiliki tingkat kepentingan tertinggi dalam klasifikasi tweet berkecenderungan bunuh diri., Suicide is one of the leading causes of death globally. Twitter data can be used in identifying internet users with suicidal tendencies. Random forest is one of the popular classification method to be used on high dimension data and on text data analysis. This study aims to apply the random forest method to predict suicidal tweets and to identify important words on suicidal tweets classification. The data consists of 10,006 tweets which has label proportion of 30:70. The research applied the random forest model by testing the hyperparameters ntree and mtry with 10- fold cross validation. This study applied oversampling and undersampling. The random forest model with oversampling with ntree value of 50 and mtry value of 176 could classify suicidal tweets with a sensitivity of 0.594 and f1-score of 0.649. Based on its mean decrease accuracy, the word ‘nyerah’ was considered as the most important word in classifying suicidal tweet this study.
Keyword: suicide, social media, random forest, text mining, text classification

Judul: Characterizations of low alloy steel sae 4422  plated with chromic acid (Cro3)
Abstrak: Characterizations of low alloy steel SAE 4422 plated with chromic acid (CrOrO3) was use Optical Microscopy (OM), Scanning Electron Microscopy (SEM), X-Ray Diffraction (XRD), RD),Energy Dispersive Analysis of X-Ray (EDAX), Hardness Test and Roughness Test. Microstructures analysis exhibits cracks and grain boundaries microcracks as a characteristics of chrome metal found at plating metal. Perlite and Ferrite as grains of steel found at base metal with grain boundaries around it. Phase analysis of plating metal appropriate to chrome metal with lattice constant (a) is 2.559 Å and phase analysis of base metal appropriate to iron ferrite with lattice constant (a) is 3.318 Å. Both plating and base metal have cubic system. Chemical analysis of plating metal at surface section exhibits that cracks area have highest value of carbon element compare with grains and grains boundaries area. While chemical analysis of base metal at cross section exhibits that perlite have highest value of carbon element compare with ferrite and grain boundaries. The hardness value of plating metal at surface section and cross section are 753 and 637 VHN. While, the hardness value of base metal and interface area at cross section are 205 and 373 VHN. The roughness value at measurement points of chrome surface for as received sample are 0.445 μm. Observation used Optical Microscopy and Scanning Electron Microscopy exhibits surface irregularities combine to form surface texture of plating metal at surface section.
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